The document proposes a selective data pruning-based compression scheme to improve rate-distortion performance. It involves pruning original frames to a smaller size before compression by dropping rows or columns. After decoding, frames are interpolated back to the original size using an edge-directed interpolation method. A novel high-order interpolation is also introduced to adapt to multiple edge directions. Simulation results validate the effectiveness of the proposed methods in image interpolation and video coding applications by achieving high quality from lower bitrates compared to existing techniques.
11.compression technique using dct fractal compressionAlexander Decker
1) The document discusses and compares different image compression techniques, specifically DCT and fractal compression.
2) Fractal compression works by finding self-similar patterns within an image during encoding, but can have a long computation time. DCT transforms an image into frequency coefficients that can be quantized for compression.
3) The document reviews previous work combining DCT and fractal compression with steganography and encryption to improve hiding capacity, imperceptibility, and security against subterfuge attacks. However, prior methods had limitations like low data hiding amounts or lack of protection for compressed data.
Compression technique using dct fractal compressionAlexander Decker
This document summarizes and compares different image compression techniques, including DCT, fractal compression, and their applications in steganography. It discusses how DCT works by transforming image data into frequency domains, while fractal compression exploits self-similarity within images. The document reviews several existing studies on combining these techniques with steganography and encryption. Specifically, it examines approaches that use DCT and fractal compression to improve data hiding capacity and security. Overall, the document provides an overview of key compression algorithms and their applications in digital watermarking and steganography.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document discusses image compression using a Raspberry Pi processor. It begins with an abstract stating that image compression is needed to reduce file sizes for storage and transmission while retaining image quality. The document then discusses various image compression techniques like discrete wavelet transform (DWT) and discrete cosine transform (DCT), as well as JPEG compression. It states that the Raspberry Pi allows implementing DWT to provide JPEG format images using OpenCV. The document provides details of the image compression method tested, which involves capturing images with a USB camera connected to the Raspberry Pi, compressing the images using DWT and wavelet transforms, transmitting the compressed images over the internet, decompressing the images on a server, and displaying the decompressed images
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
comparision of lossy and lossless image compression using various algorithmchezhiyan chezhiyan
This document compares lossy and lossless image compression using various algorithms. It discusses the need for image compression to reduce file sizes for storage and transmission. Lossy compression provides higher compression ratios but some loss of information, while lossless compression retains all information without loss. The document proposes comparing algorithms like Fractal image compression and LZW, analyzing parameters like SNR, PSNR, and MSE for formats like BMP, TIFF, PNG and JPEG. It provides details on how the LZW and Fractal compression algorithms work.
Medical Video Compression has to be loss less to avoid the danger of diagnostic errors. presentation outlines an approach to improve the compression ratio of medical video sequence using HEVC
Multiple Binary Images Watermarking in Spatial and Frequency Domainssipij
Editing, reproduction and distribution of the digital multimedia are becoming extremely easier and faster with the existence of the internet and the availability of pervasive and powerful multimedia tools. Digital watermarking has emerged as a possible method to tackle these issues. This paper proposes a scheme using which more data can be inserted into an image in different domains using different techniques. This increases the embedding capacity. Using the proposed scheme 24 binary images can be embedded in the DCT domain and 12 binary images can be embedded in the spatial domain using LSB substitution technique in a single RGB image. The proposed scheme also provides an extra level of security to the watermark image by scrambling the image before embedding it into the host image. Experimental results show that the proposed watermarking method results in almost invisible difference between the watermarked image and the original image and is also robust against various image processing attacks.
11.compression technique using dct fractal compressionAlexander Decker
1) The document discusses and compares different image compression techniques, specifically DCT and fractal compression.
2) Fractal compression works by finding self-similar patterns within an image during encoding, but can have a long computation time. DCT transforms an image into frequency coefficients that can be quantized for compression.
3) The document reviews previous work combining DCT and fractal compression with steganography and encryption to improve hiding capacity, imperceptibility, and security against subterfuge attacks. However, prior methods had limitations like low data hiding amounts or lack of protection for compressed data.
Compression technique using dct fractal compressionAlexander Decker
This document summarizes and compares different image compression techniques, including DCT, fractal compression, and their applications in steganography. It discusses how DCT works by transforming image data into frequency domains, while fractal compression exploits self-similarity within images. The document reviews several existing studies on combining these techniques with steganography and encryption. Specifically, it examines approaches that use DCT and fractal compression to improve data hiding capacity and security. Overall, the document provides an overview of key compression algorithms and their applications in digital watermarking and steganography.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document discusses image compression using a Raspberry Pi processor. It begins with an abstract stating that image compression is needed to reduce file sizes for storage and transmission while retaining image quality. The document then discusses various image compression techniques like discrete wavelet transform (DWT) and discrete cosine transform (DCT), as well as JPEG compression. It states that the Raspberry Pi allows implementing DWT to provide JPEG format images using OpenCV. The document provides details of the image compression method tested, which involves capturing images with a USB camera connected to the Raspberry Pi, compressing the images using DWT and wavelet transforms, transmitting the compressed images over the internet, decompressing the images on a server, and displaying the decompressed images
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
comparision of lossy and lossless image compression using various algorithmchezhiyan chezhiyan
This document compares lossy and lossless image compression using various algorithms. It discusses the need for image compression to reduce file sizes for storage and transmission. Lossy compression provides higher compression ratios but some loss of information, while lossless compression retains all information without loss. The document proposes comparing algorithms like Fractal image compression and LZW, analyzing parameters like SNR, PSNR, and MSE for formats like BMP, TIFF, PNG and JPEG. It provides details on how the LZW and Fractal compression algorithms work.
Medical Video Compression has to be loss less to avoid the danger of diagnostic errors. presentation outlines an approach to improve the compression ratio of medical video sequence using HEVC
Multiple Binary Images Watermarking in Spatial and Frequency Domainssipij
Editing, reproduction and distribution of the digital multimedia are becoming extremely easier and faster with the existence of the internet and the availability of pervasive and powerful multimedia tools. Digital watermarking has emerged as a possible method to tackle these issues. This paper proposes a scheme using which more data can be inserted into an image in different domains using different techniques. This increases the embedding capacity. Using the proposed scheme 24 binary images can be embedded in the DCT domain and 12 binary images can be embedded in the spatial domain using LSB substitution technique in a single RGB image. The proposed scheme also provides an extra level of security to the watermark image by scrambling the image before embedding it into the host image. Experimental results show that the proposed watermarking method results in almost invisible difference between the watermarked image and the original image and is also robust against various image processing attacks.
This paper proposes a new image compression approach that uses adaptive DCT-domain downsampling to reduce high frequency information in images for compression, and then uses learning-based mapping to compensate for the removed high frequencies during decompression. Specifically, it adaptively selects regions for downsampling in the DCT domain based on rate-distortion optimization. It then uses a database of visual patterns to map blurred image patches to corresponding high-quality patches during decompression, recovering lost high frequencies. Experimental results show it outperforms standards like H.264 and JPEG2000 especially at low bit rates.
This document summarizes a research paper on progressive image compression using wavelet transforms and SPIHT encoding. It discusses how:
1. Image compression reduces file sizes while maintaining acceptable quality, allowing more images to be stored. Wavelet transforms break images into different frequency bands, and SPIHT exploits properties of wavelet-transformed images to efficiently encode them.
2. Progressive compression methods convert images to intermediate formats and allow users to choose compressed images without noticeable quality loss. SPIHT provides fast encoding and decoding as well as embedded coding to optimize transmission.
3. SPIHT uses uniform scalar quantization and provides a simple, fast way to compress images with embedded bitstreams and progressive transmission at variable bitrates while maintaining good
REVERSIBLE WAVELET AND SPECTRAL TRANSFORMS FOR LOSSLESS COMPRESSION OF COLOR ...cscpconf
Recent years have seen tremendous increase in the generation, transmission, and storage of
color images. A new lossless image compression method for progressive-resolution
transmission of color images is carried out in this paper based on spatial and spectral
transforms. Reversible wavelet transforms are performed across red, green, and blue color sub
bands first. Then adaptive spectral transforms like inter band prediction method is applied on
associated color sub bands for image compression. The combination of inverse spectral
transform (ST-1) and inverse reversible wavelet transforms (RWT-1) finally reconstructs the
original RGB color channels exactly. Simulation of the implemented method is carried out using
MATLAB 6.5
This document discusses various image compression standards and techniques. It begins with an introduction to image compression, noting that it reduces file sizes for storage or transmission while attempting to maintain image quality. It then outlines several international compression standards for binary images, photos, and video, including JPEG, MPEG, and H.261. The document focuses on JPEG, describing how it uses discrete cosine transform and quantization for lossy compression. It also discusses hierarchical and progressive modes for JPEG. In closing, the document presents challenges and results for motion segmentation and iris image segmentation.
In this paper a novel method for image enhancement
using PDTDFB (Pyramidal Dual-Tree Directional Filter
Bank) and interpolation has been adopted. Generally, in
digital images since the different kinds of noise highly affects
various image processing techniques it is always better to
perform denoising first. Here, first of all the image is
decomposed into two different layers namely low pass sub
band and high pass sub band after which denoising is being
performed on both the layers so as to smoothen the image.
The smoothened image is then interpolated using edgepreserving
interpolation and then amplified. Finally, the HR
(High Resolution) image is being obtained by performing
image composition.
Rate Distortion Performance for Joint Source Channel Coding of JPEG image Ove...CSCJournals
1) The document discusses joint source-channel coding (JSCC) schemes for transmitting JPEG images over AWGN channels.
2) It proposes using JSCC with unequal error protection (UEP), applying stronger channel coding to important DC coefficients and weaker coding to less important AC coefficients.
3) The goal is to improve received image quality compared to equal error protection (EEP) schemes by providing varying levels of protection according to data importance.
The document discusses multimedia compression techniques. It notes that audio, image, and video files require large amounts of data, which bandwidth and storage limitations cannot accommodate for real-time transmission and playback. Compression reduces these file sizes through lossless and lossy techniques. Popular standards like JPEG and MPEG use combinations of techniques like the discrete cosine transform, quantization, predictive coding, and entropy encoding to achieve compression while maintaining quality.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
An overview Survey on Various Video compressions and its importanceINFOGAIN PUBLICATION
With the rise of digital computing and visual data processing, the need for storage and transmission of video data became prevalent. Storage and transmission of uncompressed raw visual data is not a good practice, because it requires a large storage space and great bandwidth. Video compression algorithms can compress this raw visual data or video into smaller files with a little sacrifice on the quality. This paper an overview and comparison of standard efforts on video compression algorithm of: MPEG-1, MPEG-2, MPEG-4, MPEG-7
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A Trained CNN Based Resolution Enhancement of Digital ImagesIJMTST Journal
Image Resolution Enhancement (RE) is a technique to estimate or synthesize a high resolution(HR) image
from one or several low resolution (LR) images . Resolution Enhancement (RE) technique reconstructs a
higher-resolution image or sequence from the observed LR images. In this project we are going to present
about the methods in resolution enhancement and the advancements that are taking place, since it has lot
many applications in various fields. Most resolution enhancement techniques are based on the same idea,
using information from several different images to create one upsized image. Algorithms try to extract details
from every image in a sequence to reconstruct other frames.
Abstract: The increasing amount of applications using digital multimedia technologies has accentuated the need to provide copyright protection to multimedia data. This paper reviews one of the data hiding techniques - digital image watermarking. Through this paper we will explore some basic concepts of digital image watermarking techniques.Two different methods of digital image watermarking namely spatial domain watermarking and transform domain watermarking are briefly discussed in this paper. Furthermore, two different algorithms for a digital image watermarking have also been discussed. Also the comparision between the different algorithms,tests performed for the robustness and the applications of the digital image watermarking have also been discussed.
REGION OF INTEREST BASED COMPRESSION OF MEDICAL IMAGE USING DISCRETE WAVELET ...ijcsa
Image abbreviation is utilized for reducing the size of a file without demeaning the quality of the image to an objectionable level. The depletion in file size permits more images to be deposited in a given number of spaces. It also minimizes the time necessary for images to be transferred. There are different ways of abbreviating image files. For the use of Internet, the two most common abbreviated graphic image formats are the JPEG formulation and the GIF formulation. The JPEG procedure is more often utilized or
photographs, while the GIF method is commonly used for logos, symbols and icons but at the same time
they are not preferred as they use only 256 colors. Other procedures for image compression include the
utilization of fractals and wavelets. These procedures have not profited widespread acceptance for the
utilization on the Internet. Abbreviating an image is remarkably not similar than the compressing raw
binary data. General-purpose abbreviation techniques can be utilized to compress images, the obtained
result is less than the optimal. This is because of the images have certain analytical properties, which can
be exploited by encoders specifically designed only for them. Also, some of the finer details of the image
can be renounced for the sake of storing a little more bandwidth or deposition space. In the paper,
compression is done on medical image and the compression technique that is used to perform compression
is discrete wavelet transform and discrete cosine transform which compresses the data efficiently without
reducing the quality of an image
Presentation given in the Seminar of B.Tech 6th Semester during session 2009-10 By Paramjeet Singh Jamwal, Poonam Kanyal, Rittitka Mittal and Surabhi Tyagi.
This document discusses a structural similarity based approach for efficient multi-view video coding. It begins with an introduction to multi-view video coding and the structural similarity index metric. It then proposes using structural similarity to exploit structural information between different video views. The method uses structural similarity for rate distortion optimization in encoding. Experimental results show the left and right views of a video, their structural similarity image, the decoded 3D video, and the achieved minimum distortion level. The document aims to improve multi-view video quality by using structural similarity during the encoding process.
Introduction to Video Compression Techniques - Anurag JainVideoguy
The document provides an overview of video compression techniques and standards. It discusses the motivation for video compression to reduce data sizes for storage and transmission. It then reviews several key video compression standards including H.261, H.263, MPEG-1, MPEG-2, MPEG-4, H.264 and others. For each standard, it summarizes the goals, features, applications and technical details like motion compensation methods, block sizes, and bitrate ranges.
This document proposes a multi-level block truncation code algorithm for RGB image compression to achieve low bit rates and high quality. The algorithm combines bit mapping and quantization by dividing images into blocks, calculating thresholds, quantizing thresholds, and representing blocks with bit maps. It was tested on standard images like flowers, Lena, and baboon. Results showed improved peak signal-to-noise ratio and mean squared error compared to existing methods, demonstrating the effectiveness of the proposed multi-level block truncation code algorithm for image compression.
A High Performance Modified SPIHT for Scalable Image CompressionCSCJournals
In this paper, we present a novel extension technique to the Set Partitioning in Hierarchical Trees (SPIHT) based image compression with spatial scalability. The present modification and the preprocessing techniques provide significantly better quality (both subjectively and objectively) reconstruction at the decoder with little additional computational complexity. There are two proposals for this paper. Firstly, we propose a pre-processing scheme, called Zero-Shifting, that brings the spatial values in signed integer range without changing the dynamic ranges, so that the transformed coefficient calculation becomes more consistent. For that reason, we have to modify the initialization step of the SPIHT algorithms. The experiments demonstrate a significant improvement in visual quality and faster encoding and decoding than the original one. Secondly, we incorporate the idea to facilitate resolution scalable decoding (not incorporated in original SPIHT) by rearranging the order of the encoded output bit stream. During the sorting pass of the SPIHT algorithm, we model the transformed coefficient based on the probability of significance, at a fixed threshold of the offspring. Calling it a fixed context model and generating a Huffman code for each context, we achieve comparable compression efficiency to that of arithmetic coder, but with much less computational complexity and processing time. As far as objective quality assessment of the reconstructed image is concerned, we have compared our results with popular Peak Signal to Noise Ratio (PSNR) and with Structural Similarity Index (SSIM). Both these metrics show that our proposed work is an improvement over the original one.
Thesis on Image compression by Manish MystManish Myst
The document discusses using neural networks for image compression. It describes how previous neural network methods divided images into blocks and achieved limited compression. The proposed method applies edge detection, thresholding, and thinning to images first to reduce their size. It then uses a single-hidden layer feedforward neural network with an adaptive number of hidden neurons based on the image's distinct gray levels. The network is trained to compress the preprocessed image block and reconstruct the original image at the receiving end. This adaptive approach aims to achieve higher compression ratios than previous neural network methods.
Video Denoising using Transform Domain MethodIRJET Journal
This document presents a proposed method for video denoising using dictionary learning and transform domain techniques. It begins with an abstract describing how traditional video denoising models based on Gaussian noise do not account for real-world noise sources. The proposed method then learns basis functions adaptively from input video frames using dictionary learning, providing a sparse representation. Hard thresholding is applied in the transform domain to compute denoised frames. Experimental results on standard test videos show the method achieves competitive performance compared to other approaches in terms of peak signal-to-noise ratio.
This document discusses digital image processing and image compression. It covers 5 units: digital image fundamentals, image transforms, image enhancement, image filtering and restoration, and image compression. Image compression aims to reduce the size of image data and is important for applications like facsimile transmission and CD-ROM storage. There are two types of compression - lossless, where the original and reconstructed data are identical, and lossy, which allows some loss for higher compression ratios. Factors to consider for compression method selection include whether lossless or lossy is needed, coding efficiency, complexity tradeoffs, and the application.
This paper proposes a new image compression approach that uses adaptive DCT-domain downsampling to reduce high frequency information in images for compression, and then uses learning-based mapping to compensate for the removed high frequencies during decompression. Specifically, it adaptively selects regions for downsampling in the DCT domain based on rate-distortion optimization. It then uses a database of visual patterns to map blurred image patches to corresponding high-quality patches during decompression, recovering lost high frequencies. Experimental results show it outperforms standards like H.264 and JPEG2000 especially at low bit rates.
This document summarizes a research paper on progressive image compression using wavelet transforms and SPIHT encoding. It discusses how:
1. Image compression reduces file sizes while maintaining acceptable quality, allowing more images to be stored. Wavelet transforms break images into different frequency bands, and SPIHT exploits properties of wavelet-transformed images to efficiently encode them.
2. Progressive compression methods convert images to intermediate formats and allow users to choose compressed images without noticeable quality loss. SPIHT provides fast encoding and decoding as well as embedded coding to optimize transmission.
3. SPIHT uses uniform scalar quantization and provides a simple, fast way to compress images with embedded bitstreams and progressive transmission at variable bitrates while maintaining good
REVERSIBLE WAVELET AND SPECTRAL TRANSFORMS FOR LOSSLESS COMPRESSION OF COLOR ...cscpconf
Recent years have seen tremendous increase in the generation, transmission, and storage of
color images. A new lossless image compression method for progressive-resolution
transmission of color images is carried out in this paper based on spatial and spectral
transforms. Reversible wavelet transforms are performed across red, green, and blue color sub
bands first. Then adaptive spectral transforms like inter band prediction method is applied on
associated color sub bands for image compression. The combination of inverse spectral
transform (ST-1) and inverse reversible wavelet transforms (RWT-1) finally reconstructs the
original RGB color channels exactly. Simulation of the implemented method is carried out using
MATLAB 6.5
This document discusses various image compression standards and techniques. It begins with an introduction to image compression, noting that it reduces file sizes for storage or transmission while attempting to maintain image quality. It then outlines several international compression standards for binary images, photos, and video, including JPEG, MPEG, and H.261. The document focuses on JPEG, describing how it uses discrete cosine transform and quantization for lossy compression. It also discusses hierarchical and progressive modes for JPEG. In closing, the document presents challenges and results for motion segmentation and iris image segmentation.
In this paper a novel method for image enhancement
using PDTDFB (Pyramidal Dual-Tree Directional Filter
Bank) and interpolation has been adopted. Generally, in
digital images since the different kinds of noise highly affects
various image processing techniques it is always better to
perform denoising first. Here, first of all the image is
decomposed into two different layers namely low pass sub
band and high pass sub band after which denoising is being
performed on both the layers so as to smoothen the image.
The smoothened image is then interpolated using edgepreserving
interpolation and then amplified. Finally, the HR
(High Resolution) image is being obtained by performing
image composition.
Rate Distortion Performance for Joint Source Channel Coding of JPEG image Ove...CSCJournals
1) The document discusses joint source-channel coding (JSCC) schemes for transmitting JPEG images over AWGN channels.
2) It proposes using JSCC with unequal error protection (UEP), applying stronger channel coding to important DC coefficients and weaker coding to less important AC coefficients.
3) The goal is to improve received image quality compared to equal error protection (EEP) schemes by providing varying levels of protection according to data importance.
The document discusses multimedia compression techniques. It notes that audio, image, and video files require large amounts of data, which bandwidth and storage limitations cannot accommodate for real-time transmission and playback. Compression reduces these file sizes through lossless and lossy techniques. Popular standards like JPEG and MPEG use combinations of techniques like the discrete cosine transform, quantization, predictive coding, and entropy encoding to achieve compression while maintaining quality.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
An overview Survey on Various Video compressions and its importanceINFOGAIN PUBLICATION
With the rise of digital computing and visual data processing, the need for storage and transmission of video data became prevalent. Storage and transmission of uncompressed raw visual data is not a good practice, because it requires a large storage space and great bandwidth. Video compression algorithms can compress this raw visual data or video into smaller files with a little sacrifice on the quality. This paper an overview and comparison of standard efforts on video compression algorithm of: MPEG-1, MPEG-2, MPEG-4, MPEG-7
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
A Trained CNN Based Resolution Enhancement of Digital ImagesIJMTST Journal
Image Resolution Enhancement (RE) is a technique to estimate or synthesize a high resolution(HR) image
from one or several low resolution (LR) images . Resolution Enhancement (RE) technique reconstructs a
higher-resolution image or sequence from the observed LR images. In this project we are going to present
about the methods in resolution enhancement and the advancements that are taking place, since it has lot
many applications in various fields. Most resolution enhancement techniques are based on the same idea,
using information from several different images to create one upsized image. Algorithms try to extract details
from every image in a sequence to reconstruct other frames.
Abstract: The increasing amount of applications using digital multimedia technologies has accentuated the need to provide copyright protection to multimedia data. This paper reviews one of the data hiding techniques - digital image watermarking. Through this paper we will explore some basic concepts of digital image watermarking techniques.Two different methods of digital image watermarking namely spatial domain watermarking and transform domain watermarking are briefly discussed in this paper. Furthermore, two different algorithms for a digital image watermarking have also been discussed. Also the comparision between the different algorithms,tests performed for the robustness and the applications of the digital image watermarking have also been discussed.
REGION OF INTEREST BASED COMPRESSION OF MEDICAL IMAGE USING DISCRETE WAVELET ...ijcsa
Image abbreviation is utilized for reducing the size of a file without demeaning the quality of the image to an objectionable level. The depletion in file size permits more images to be deposited in a given number of spaces. It also minimizes the time necessary for images to be transferred. There are different ways of abbreviating image files. For the use of Internet, the two most common abbreviated graphic image formats are the JPEG formulation and the GIF formulation. The JPEG procedure is more often utilized or
photographs, while the GIF method is commonly used for logos, symbols and icons but at the same time
they are not preferred as they use only 256 colors. Other procedures for image compression include the
utilization of fractals and wavelets. These procedures have not profited widespread acceptance for the
utilization on the Internet. Abbreviating an image is remarkably not similar than the compressing raw
binary data. General-purpose abbreviation techniques can be utilized to compress images, the obtained
result is less than the optimal. This is because of the images have certain analytical properties, which can
be exploited by encoders specifically designed only for them. Also, some of the finer details of the image
can be renounced for the sake of storing a little more bandwidth or deposition space. In the paper,
compression is done on medical image and the compression technique that is used to perform compression
is discrete wavelet transform and discrete cosine transform which compresses the data efficiently without
reducing the quality of an image
Presentation given in the Seminar of B.Tech 6th Semester during session 2009-10 By Paramjeet Singh Jamwal, Poonam Kanyal, Rittitka Mittal and Surabhi Tyagi.
This document discusses a structural similarity based approach for efficient multi-view video coding. It begins with an introduction to multi-view video coding and the structural similarity index metric. It then proposes using structural similarity to exploit structural information between different video views. The method uses structural similarity for rate distortion optimization in encoding. Experimental results show the left and right views of a video, their structural similarity image, the decoded 3D video, and the achieved minimum distortion level. The document aims to improve multi-view video quality by using structural similarity during the encoding process.
Introduction to Video Compression Techniques - Anurag JainVideoguy
The document provides an overview of video compression techniques and standards. It discusses the motivation for video compression to reduce data sizes for storage and transmission. It then reviews several key video compression standards including H.261, H.263, MPEG-1, MPEG-2, MPEG-4, H.264 and others. For each standard, it summarizes the goals, features, applications and technical details like motion compensation methods, block sizes, and bitrate ranges.
This document proposes a multi-level block truncation code algorithm for RGB image compression to achieve low bit rates and high quality. The algorithm combines bit mapping and quantization by dividing images into blocks, calculating thresholds, quantizing thresholds, and representing blocks with bit maps. It was tested on standard images like flowers, Lena, and baboon. Results showed improved peak signal-to-noise ratio and mean squared error compared to existing methods, demonstrating the effectiveness of the proposed multi-level block truncation code algorithm for image compression.
A High Performance Modified SPIHT for Scalable Image CompressionCSCJournals
In this paper, we present a novel extension technique to the Set Partitioning in Hierarchical Trees (SPIHT) based image compression with spatial scalability. The present modification and the preprocessing techniques provide significantly better quality (both subjectively and objectively) reconstruction at the decoder with little additional computational complexity. There are two proposals for this paper. Firstly, we propose a pre-processing scheme, called Zero-Shifting, that brings the spatial values in signed integer range without changing the dynamic ranges, so that the transformed coefficient calculation becomes more consistent. For that reason, we have to modify the initialization step of the SPIHT algorithms. The experiments demonstrate a significant improvement in visual quality and faster encoding and decoding than the original one. Secondly, we incorporate the idea to facilitate resolution scalable decoding (not incorporated in original SPIHT) by rearranging the order of the encoded output bit stream. During the sorting pass of the SPIHT algorithm, we model the transformed coefficient based on the probability of significance, at a fixed threshold of the offspring. Calling it a fixed context model and generating a Huffman code for each context, we achieve comparable compression efficiency to that of arithmetic coder, but with much less computational complexity and processing time. As far as objective quality assessment of the reconstructed image is concerned, we have compared our results with popular Peak Signal to Noise Ratio (PSNR) and with Structural Similarity Index (SSIM). Both these metrics show that our proposed work is an improvement over the original one.
Thesis on Image compression by Manish MystManish Myst
The document discusses using neural networks for image compression. It describes how previous neural network methods divided images into blocks and achieved limited compression. The proposed method applies edge detection, thresholding, and thinning to images first to reduce their size. It then uses a single-hidden layer feedforward neural network with an adaptive number of hidden neurons based on the image's distinct gray levels. The network is trained to compress the preprocessed image block and reconstruct the original image at the receiving end. This adaptive approach aims to achieve higher compression ratios than previous neural network methods.
Video Denoising using Transform Domain MethodIRJET Journal
This document presents a proposed method for video denoising using dictionary learning and transform domain techniques. It begins with an abstract describing how traditional video denoising models based on Gaussian noise do not account for real-world noise sources. The proposed method then learns basis functions adaptively from input video frames using dictionary learning, providing a sparse representation. Hard thresholding is applied in the transform domain to compute denoised frames. Experimental results on standard test videos show the method achieves competitive performance compared to other approaches in terms of peak signal-to-noise ratio.
This document discusses digital image processing and image compression. It covers 5 units: digital image fundamentals, image transforms, image enhancement, image filtering and restoration, and image compression. Image compression aims to reduce the size of image data and is important for applications like facsimile transmission and CD-ROM storage. There are two types of compression - lossless, where the original and reconstructed data are identical, and lossy, which allows some loss for higher compression ratios. Factors to consider for compression method selection include whether lossless or lossy is needed, coding efficiency, complexity tradeoffs, and the application.
Image compression and reconstruction using improved Stockwell transform for q...IJECEIAES
Image compression is an important stage in picture processing since it reduces the data extent and promptness of image diffusion and storage, whereas image reconstruction helps to recover the original information that was communicated. Wavelets are commonly cited as a novel technique for image compression, although the production of waves proceeding smooth areas with the image remains unsatisfactory. Stockwell transformations have been recently entered the arena for image compression and reconstruction operations. As a result, a new technique for image compression based on the improved Stockwell transform is proposed. The discrete cosine transforms, which involves bandwidth partitioning is also investigated in this work to verify its experimental results. Wavelet-based techniques such as multilevel Haar wavelet, generic multiwavelet transform, Shearlet transform, and Stockwell transforms were examined in this paper. The MATLAB technical computing language is utilized in this work to implement the existing approaches as well as the suggested improved Stockwell transform. The standard images mostly used in digital image processing applications, such as Lena, Cameraman and Barbara are investigated in this work. To evaluate the approaches, quality constraints such as mean square error (MSE), normalized cross-correlation (NCC), structural content (SC), peak noise ratio, average difference (AD), normalized absolute error (NAE) and maximum difference are computed and provided in tabular and graphical representations.
Conference Proceedings of the National Level Technical Symposium on Emerging Trends in Technology, TECHNOVISION ’10, G.N.D.E.C. Ludhiana, Punjab, India- 9th-10th April, 2010
The main aim of image compression is to represent the image with minimum number of bits and thus reduce the size of the image. This paper presents a Symbols Frequency based Image Coding (SFIC) technique for image compression. This method utilizes the frequency of occurrence of pixels in an image. A frequency factor, y is used to merge y pixel values that are in the same range. In this approach, the pixel values of the image that are within the frequency factor, y range are clubbed to the least pixel value in the set. As a result, there is omission of larger pixel values and hence the total size of the image reduces and thus results in higher compression ratio. It is noticed that the selection of the frequency factor, y has a great influence on the performance of the proposed scheme. However, higher PSNR values are obtained since the omitted pixels are mapped to pixels in the similar range. The proposed approach is analyzed with quantization and without quantization. The results are analyzed. This proposed new compression model is compared with Quadtree-segmented AMBTC with Bit Map Omission. From the experimental analysis it is observed that the proposed SFIC image compression scheme with both lossless and lossy techniques outperforms AMBTC-QTBO. Hence, the proposed new compression model is a better choice for lossless and lossy compression applications.
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Iaetsd performance analysis of discrete cosineIaetsd Iaetsd
The document discusses image compression using the discrete cosine transform (DCT). It provides background on image compression and outlines the DCT technique. The DCT transforms an image into elementary frequency components, removing spatial redundancy. The document analyzes the performance of compressing different images using DCT in Matlab by measuring metrics like PSNR. Compression using DCT with different window sizes achieved significant PSNR values.
This document discusses various image compression methods and algorithms. It begins by explaining the need for image compression in applications like transmission, storage, and databases. It then reviews different types of compression, including lossless techniques like run length encoding and Huffman encoding, and lossy techniques like transformation coding, vector quantization, fractal coding, and subband coding. The document also describes the JPEG 2000 image compression algorithm and applications of JPEG 2000. Finally, it discusses self-organizing feature maps (SOM) and learning vector quantization (VQ) for image compression.
This document presents a novel approach for jointly optimizing spatial prediction and transform coding in video compression. It aims to improve performance and reduce complexity compared to existing techniques. The proposed method uses singular value decomposition (SVD) to compress images. SVD decomposes an image matrix into three matrices, allowing the image to be approximated using only a few singular values. This achieves compression by removing redundant information. The document outlines the SVD approach for image compression and measures compression performance using compression ratio and mean squared error between the original and compressed images. It then discusses trends in image and video coding, including combining natural and synthetic content. Finally, it provides a block diagram of the proposed system and compares its compression performance to existing discrete cosine transform-
Enhanced Image Compression Using WaveletsIJRES Journal
Data compression which can be lossy or lossless is required to decrease the storage requirement and better data transfer rate. One of the best image compression techniques is using wavelet transform. It is comparatively new and has many advantages over others. Wavelet transform uses a large variety of wavelets for decomposition of images. The state of the art coding techniques like HAAR, SPIHT (set partitioning in hierarchical trees) and use the wavelet transform as basic and common step for their own further technical advantages. The wavelet transform results therefore have the importance which is dependent on the type of wavelet used .In our thesis we have used different wavelets to perform the transform of a test image and the results have been discussed and analyzed. Haar, Sphit wavelets have been applied to an image and results have been compared in the form of qualitative and quantitative analysis in terms of PSNR values and compression ratios. Elapsed times for compression of image for different wavelets have also been computed to get the fast image compression method. The analysis has been carried out in terms of PSNR (peak signal to noise ratio) obtained and time taken for decomposition and reconstruction.
Reversible Data Hiding Using Contrast Enhancement ApproachCSCJournals
Reverse Data Hiding is a technique used to hide the object's data details. This technique is used to ensure the security and to protect the integrity of the object from any modification by preventing intended and unintended changes. Digital watermarking is a key ingredient to multimedia protection. However, most existing techniques distort the original content as a side effect of image protection. As a way to overcome such distortion, reversible data embedding has recently been introduced and is growing rapidly. In reversible data embedding, the original content can be completely restored after the removal of the watermark. Therefore, it is very practical to protect legal, medical, or other important imagery. In this paper a novel removable (lossless) data hiding technique is proposed. This technique is based on the histogram modification to produce extra space for embedding, and the redundancy in digital images is exploited to achieve a very high embedding capacity. This method has been applied to various standard images. The experimental results have demonstrated a promising outcome and the proposed technique achieved satisfactory and stable performance both on embedding capacity and visual quality. The proposed method capacity is up to 129K bits with PSNR between 42-45dB. The performance is hence better than most exiting reversible data hiding algorithms.
Design and Implementation of EZW & SPIHT Image Coder for Virtual ImagesCSCJournals
The main objective of this paper is to designed and implemented a EZW & SPIHT Encoding Coder for Lossy virtual Images. .Embedded Zero Tree Wavelet algorithm (EZW) used here is simple, specially designed for wavelet transform and effective image compression algorithm. This algorithm is devised by Shapiro and it has property that the bits in the bit stream are generated in order of importance, yielding a fully embedded code. SPIHT stands for Set Partitioning in Hierarchical Trees. The SPIHT coder is a highly refined version of the EZW algorithm and is a powerful image compression algorithm that produces an embedded bit stream from which the best reconstructed images. The SPIHT algorithm was powerful, efficient and simple image compression algorithm. By using these algorithms, the highest PSNR values for given compression ratios for a variety of images can be obtained. SPIHT was designed for optimal progressive transmission, as well as for compression. The important SPIHT feature is its use of embedded coding. The pixels of the original image can be transformed to wavelet coefficients by using wavelet filters. We have anaysized our results using MATLAB software and wavelet toolbox and calculated various parameters such as CR (Compression Ratio), PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error), and BPP (Bits per Pixel). We have used here different Wavelet Filters such as Biorthogonal, Coiflets, Daubechies, Symlets and Reverse Biorthogonal Filters .In this paper we have used one virtual Human Spine image (256X256).
The document summarizes an efficient image compression technique using Overlapped Discrete Cosine Transform (MDCT) combined with adaptive thinning.
In the first phase, MDCT is applied which is based on DCT-IV but with overlapping blocks, enabling robust compression. In the second phase, adaptive thinning recursively removes points from the image based on Delaunay triangulations, further compressing the image. Simulation results showed over 80% pixel reduction with 30dB PSNR, requiring less points for the compressed image. The technique combines MDCT for frequency-domain compression with adaptive thinning for spatial-domain compression.
Image compression techniques by using wavelet transformAlexander Decker
This document discusses image compression techniques using wavelet transforms. It begins with an introduction to image compression and discusses lossless and lossy compression methods. It then focuses on wavelet transforms, which decompose images into different frequency components, allowing for better compression. The document describes how wavelet-based compression avoids blocking artifacts seen in other methods like DCT. It details an image compression program called MinImage that implements various wavelet types and the embedded zerotree wavelet coding algorithm to achieve good compression ratios while maintaining image quality. In conclusion, wavelet transforms combined with entropy coding provide effective lossy compression of digital images.
This document summarizes various image compression techniques. It discusses lossless compression techniques like run length encoding, entropy encoding, and area coding that allow perfect reconstruction of images. It also discusses lossy compression techniques like chroma subsampling, transform coding, and fractal compression that allow reconstruction of images with some loss of quality in exchange for higher compression ratios. These lossy techniques are suitable for natural images like photographs. The document provides examples and explanations of how several common compression techniques work.
This document compares the performance of image restoration techniques in the time and frequency domains. It proposes a new algorithm to denoise images corrupted by salt and pepper noise. The algorithm replaces noisy pixel values within a 3x3 window with a weighted median based on neighboring pixels. It applies filters like CLAHE, average, Wiener and median filtering before the proposed algorithm to further remove noise. Experimental results on test images show the proposed method achieves better noise removal compared to other techniques, with around a 60% increase in PSNR and 90% reduction in MSE. In conclusion, the proposed algorithm is effective at restoring images with high density salt and pepper noise.
DISCRETE COSINE TRANSFORM WITH ADAPTIVE HUFFMAN CODING BASED IMAGE COMPRESSION
code with ressult
ABSTRACT
Method of compression which is Huffman coding based on histogram information and image segmentation. It is used for lossless and lossy compression. Theamount of image will be compressed in lossy manner, and in lossless manner, depends on theinformation obtained by the histogram of the image. The results show that the difference betweenoriginal and compressed images is visually negligible. The compression ratio(CR) and peak signal tonoise ratio(PSNR) are obtained for different images. The relation between compression ratio and peaksignal to noise ratio shows that whenever we increase compression ratio we get PSNR high. We can alsoobtain minimum mean square error. It shows that if we get high PSNR than our image quality is better.
This document summarizes a presentation on wavelet based image compression. It begins with an introduction to image compression, describing why it is needed and common techniques like lossy and lossless compression. It then discusses wavelet transforms and how they are applied to image compression. Several research papers on wavelet compression techniques are reviewed and key advantages like higher compression ratios while maintaining image quality are highlighted. Applications of wavelet compression in areas like biomedicine and multimedia are presented before concluding with references.
Comparison of different Fingerprint Compression Techniquessipij
The important features of wavelet transform and different methods in compression of fingerprint images have been implemented. Image quality is measured objectively using peak signal to noise ratio (PSNR) and mean square error (MSE).A comparative study using discrete cosine transform based Joint Photographic Experts Group(JPEG) standard , wavelet based basic Set Partitioning in Hierarchical trees(SPIHT) and Modified SPIHT is done. The comparison shows that Modified SPIHT offers better compression than basic SPIHT and JPEG. The results will help application developers to choose a good wavelet compression system for their applications.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Trusted Execution Environment for Decentralized Process MiningLucaBarbaro3
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
2. 400 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 2, FEBRUARY 2010
its LR version. Instead of directly interpolating the HR image
in pixel domain, zeros were initially padded for the high fre-
quencies from the wavelet transform [10] and the courtourlet
Fig. 1. Block diagram of the data pruned-based compression.
transform [11]. These algorithms were then iterated under the
constraints of sparsity and the similarity of low pass output of
the LR and HR images.
In demosaicking, interpolation is applied to reconstruct the frequencies. Only 25% of the data is kept in the pruning phase,
missing color component due to color-filtered image sensor. The fact that also prevents achieving a reconstructed frame with high
full-resolution color image can be achieved from the Bayer color quality, even without compression.
filter array by interpolating the (R,G,B) planes separately as in In data pruning-based compression for video, downsizing in
[7] or jointly as in [12], [13]. Processing the color plane indepen- both spatial and temporal direction is applied to further reduce
dently helps avoiding the misregistration between color planes the bit-rate. In temporally data pruning-based compression, the
but ignores the color planes’ dependency. For joinly color plane frame-rate is usually reduced by half and is later reconstructed
interpolation, the green pixels are first interpolated from the can- by motion compensated frame interpolation (MCFI) methods
didates of horizontal and vertical interpolation. After that, red [16], [17] . For fast motion video sequences or for frames at
and blue pixels are reconstructed based on the color differences scene change, these methods typically cause blocking, flick-
and with assumption that these differences are flat ering and ghosting artifacts. The rate-distortion (R-D) relation
over the small areas. An iterative algorithm for demosaicking of these data pruned compressed sequences is much lower than
using the color difference is discussed in [14]. Interpolation is that of the directly compressed sequences due to the high per-
also required when video sequences are displayed in different centage of data loss (up to 87.5%) and the limitation of current
frame sizes other than its original frame size. In [15], the de- video interpolation methods.
coded frame in unsuitable frame size is upsized and downsized Uniformly pruning image or video sequences ignores the
to achieved the arbitrary target frame size in a pixel domain data-dependent artifacts caused by the interpolation phase. In
transcoder. this paper, the proposed data prune-based compression method
When interpolation is used along with data pruning, the adapts to the error resulted from the interpolation phase. Data
method needs to adapt to the way of pruning the data and to the which can be reconstructed with less error has higher priority
structure of surrounding pixels. For instance, there are pruning to be dropped than data which cause higher error during the
cases in which only rows or only columns are dropped and interpolation. The proposed data pruning phase and its cor-
upsampling in only one direction is required. This paper de- responding interpolation phase in Fig. 1 will be discussed in
velops a high-order edge-directed interpolation scheme to deal Sections III and IV, respectively.
with these cases. The algorithm is also considered for the cases
of dropping both rows and columns. Furthermore, instead of III. OPTIMAL DATA PRUNING
using only spatially neighboring pixels for image interpolation, The block diagram of the data pruning phase for one frame
the algorithm is extended for cases of video interpolation using is shown in Fig. 2. Only the even rows and columns may be
spatio-temporally neighboring pixels. discarded, while the odd rows and columns are always kept for
The paper is organized as follows. Section II introduces the later interpolation. To simplify the analysis, the compression
data pruning-based compression method. Section III derives stage in Fig. 1 is ignored. In this phase, the original frame is
an optimal data pruning algorithm. The high-order edge-di- selectively decimated to the LR frame for cases of dropping
rected interpolation methods which are corresponding to the all the even rows, all the even columns and all the even rows and
data pruning-based compression scheme are described in Sec- columns. Then, for each of these 3 downsampling scenarios,
tion IV. Results for interpolation and coding applications are is interpolated back to the HR frame based on all odd
presented in Section V. Finally, Section VI gives the concluding rows and columns (upscaling by ratio of 2 2) or all odd rows
remarks and discusses future works. (upscaling by ratio of 2 1) or all odd columns (upscaling by
ratio of 1 2). Finally, these 3 reconstructed are compared to
II. DATA PRUNE-BASED COMPRESSION in order to decide the best downsampling scenario and number
of even rows and columns to be dropped before compression.
The block diagram of the data pruning-based compression for Because of the decimation and interpolation, the reconstructed
one frame is shown in Fig. 1. At first, the original frame of size frame is different than its original frame . The principle of
is pruned to frame of smaller size the algorithm is that the even rows and columns in that have
, where and are the number of dropped rows and least error compared to its corresponding rows and columns in
columns, respectively. The purpose of data pruning is to reduce are chosen to be dropped. The mean squared error
the number of bits representing the stored or compressed frame between and is defined as
. Then, frame having the original size is reconstructed
by interpolating . The conventional data pruning-based com-
(1)
pression methods reduce the frame size with a factor of 2 in both
horizontal and vertical direction by dropping half of the columns
and rows. Because of aliasing, interpolation after downsizing Given a target , the data pruning is optimized to
causes jaggedness artifacts, especially for detail areas with high discard the maximum number of pixels while keeping the
Authorized licensed use limited to: Univ of Calif San Diego. Downloaded on April 02,2010 at 13:47:56 EDT from IEEE Xplore. Restrictions apply.
3. VÕ et al.: SELECTIVE DATA PRUNING-BASED COMPRESSION USING HIGH-ORDER EDGE-DIRECTED INTERPOLATION 401
Fig. 2. Block diagram of the data pruning phase.
overall of dropping rows and columns less
than , that is
Fig. 3. Data pruning for the 1st frame of Akiyo sequence. (a) Lines indicated
for pruning. (b) Pruned frame.
(2)
The location of the dropped rows and columns is indicated by quence Akiyo. In Fig. 3(a), the white lines indicate the dropped
and , respectively. If the even column is dropped, lines with the target dB. The frame size is re-
then , otherwise . These indices are stored duced from the standard definition 720 480 to 464 320. The
as side information in the coded bitstream and are used for re- data pruned frame in Fig. 3(b) is more compact and it requires a
constructing the decoded frame. The same algorithm is applied smaller compressed bitstream than the original frame. Most of
to rows. dropped lines are located in flat areas where the aliasing does
The line mean square error for one dropped column not happen.
is defined as For video sequences, the algorithm is extended by dropping
the same lines over frames in the whole group of picture
. In this case, the is defined as
(3)
and similarly for rows. From (2), lines with smaller
have higher priority to be dropped than lines with larger (6)
. Assume that the rows and columns that are where and are the original and reconstructed video se-
dropped have the smallest and that the maximum quences, respectively, and is the number of frames in the
of these lines is . Then, the overall . The for one dropped column is also extended
in (1) becomes the averaged of all dropped pixels [see in the temporal direction as
(4), shown at the bottom of the page]. Therefore, the condition
in (2) can be tightened to (7)
and similarly for rows. This case leads to the same condition as
in (5).
(5)
IV. HIGH-ORDER EDGE-DIRECTED INTERPOLATION
where is the target minimal that the recon- This section proposes a high-order edge-directed interpola-
structed frame has to achieve. An example of the proposed op- tion method to interpolate the downsized frames in Fig. 1
timal data pruning is shown in Fig. 3 for the 1st frame of the se- and the data pruned frames in Fig. 2. In [7], the fourth-order
(4)
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4. 402 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 2, FEBRUARY 2010
Fig. 4. Block diagram of the single frame-based interpolation phase.
new edge-directed interpolation (NEDI-4) is used to upsize only
for the 2 2 ratio. This interpolation can orient to edges in
2 directions and causes some artifacts in the intersections of
more than 2 edges. The proposed methods are higher order in-
terpolations that can adapt to more edge directions. For single Fig. 5. Model parameters of sixth-order and eighth-order edge-directed inter-
frame-based interpolation, the sixth-order edge-directed inter- polation. (a) NEDI-6. (b) NEDI-8.
polation and eighth-order interpolation are developed for in-
terpolating the cases with ratio 1 2 or 2 1 (dropping only , the optimal minimizing the MSE between the interpo-
rows or only columns) and ratio 2 2 (dropping both rows and lated and original pixels in can be calculated by
columns), respectively. For multiframe-based interpolation, the
ninth-order edge-directed interpolation is discussed for interpo-
(9)
lating the case with ratio 1 2 or 2 1 over all the frames of a
GOP (dropping only rows or only columns).
The geometric duality assumption [18] states that the model
A. Single Frame-Based Interpolation vector can be considered constant for different scales and
Because the similar interpolation method is used for and so, it can be estimated from the LR pixels by
, this section will only discuss the case of interpolating .
The block diagram of the interpolation phase is shown in Fig. 4.
First, is expanded to of size by inserting a line
of zeros at the line of if its indicator value
for columns or for rows. is selectively down-
sampled by 1 2, 2 1 or 2 2 ratio to form depending (10)
on the chosen data pruning scheme. Then, are directionally
interpolated to the HR frame of size . Finally, the where are 6-neighboring LR pixels of and
indicators and determine whether the lines in the final is the LR model parameter vector as shown in Fig. 5(a).
reconstructed frame are selected from the interpolated or from contains the edge-directed information which is applied to the
the data pruned frame HR scale for interpolation. The optimal minimum MSE linear
is then obtained by
if
otherwise. (11)
1) Sixth-Order Edge-Directed Interpolation (NEDI- ): The where is the vector of all mapped LR pixels
same NEDI-6 is implemented for case of single frame-based in and is a matrix. The elements of the column
interpolation with upsampling ratios of 1 2 or 2 1. For the of are the 6-neighboring pixels of shown in
case of ratio 1 2, the pixel indexes are classified to Fig. 5(a).
indexes for odd columns and indexes for odd columns 2) Eighth-Order Edge-Directed Interpolation (NEDI- ):
. The columns of are mapped to the odd columns of the This section develops an algorithm to deal with single
HR frame of size by . The frame-based interpolation for the case of upsampling with
even columns of are interpolated from the odd columns by ratio of 2 2. Similar to NEDI-6, the pixels in corre-
a sixth-order interpolation sponding to the LR pixels downsampling by 2 2 in are
extracted to form the LR frame of size . The
interpolation is performed using NEDI-4 as in [7] for the first
round and NEDI-8 for the second round. The interpolation
schemes of NEDI-4 and NEDI-8 are shown in Fig. 5(b), where
(8) the solid circles are the mapped LR pixels and the other pixels
are the HR pixels to be interpolated. Using the quincunx
where is the vector of sixth-order model parameters and sublattice, two passes are performed in the first round. In the
is the vector of 6-neighboring pixels of as shown in first pass, NEDI-4 is used to interpolate type 1 pixels (squares
Fig. 5(a). In this figure, the solid circles are the mapped LR with lines) from the LR pixels (solid circles). In the second
pixels while the circles are the HR pixels needed to be inter- pass, type 2 pixels (squares) and type 3 pixels (circles) are
polated. Assuming that is nearly constant in a local window interpolated from type 1 and LR pixels.
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5. VÕ et al.: SELECTIVE DATA PRUNING-BASED COMPRESSION USING HIGH-ORDER EDGE-DIRECTED INTERPOLATION 403
Fig. 6. Block diagram of the proposed multiframe-based interpolation for case of upsampling with ratio 1 2 2.
Having an initial estimation of all the 8-neighboring pixels, based on the sum of absolute difference between
NEDI-8 is implemented to get extra information from 4 direc- the current block and its matching block
tions in the second round. In this round, the model parame-
ters can be directly estimated from the HR pixels. Therefore,
the overfitting problem of NEDI-4 is reduced while considering
more edge orientations. For the sake of interpolation consis-
tency, NEDI-8 is applied to the pixels of type 3, 2, and 1 as in this (12)
order. The fourth-order model parameters and eighth-order
model parameters for HR scale are shown in Fig. 5(b). The where is the block of pixels of interest that includes the HR
optimal is similarly calculated by (11), where is the vector pixels needed to interpolate and is the motion vector
of all HR pixels in , and matrix is of block . is calculated by
employed, which is a matrix whose column is com-
if
posed of the 8-neighboring pixels of .
otherwise
B. Multiframe-Based Interpolation where is the threshold to determine whether is chosen
For multiframe interpolation, using single-frame-based inter- from or . The final reconstructed frame is
polation algorithm such as NEDI-6 or NEDI-8 can result in selected from the interpolated frame or the data pruned frame
temporal inconsistency. This comes from ignoring of temporal by the indicators and
correlation of the single-frame-based interpolation. A spatio-
temporal interpolation method is proposed in this subsection
to reduce the flickering effect. To interpolate one HR pixel in if
the current frame, extra surrounding pixels from the previous otherwise.
frame are used together with its surrounding pixels in the cur- 2) Ninth-Order Edge-Directed Interpolation (NEDI- ): In
rent frame. A multiframe-based ninth-order edge-directed inter- NEDI-9, besides the 6 surrounding pixels in the current frame,
polation (NEDI-9) method is discussed for the case of dropping 3 more pixels in the matching block of previous frame are used.
all the even columns over frames of the whole GOP. A similar The interpolation phase is implemented as shown in Fig. 4. The
algorithm can be applied to the cases of dropping all even rows interpolated pixel is the weighted average
or both even columns and rows.
1) Spatio-Temporal Interpolation Scheme: The block dia-
gram of the multiframe-based interpolation is shown in Fig. 6.
First, the current compressed data pruned frame is ex-
panded to of the original size by inserting zeros as in Sub-
section IV-A. Then, is single frame-based interpolated to
using NEDI-6 as in (8). Assume that the previous inter-
polated frame is , a block-based motion estimation and
motion compensation are used to align the block of pixels of in-
terest in to its matching block in . Interpolating (13)
the current frame and motion estimating based on larger blocks
help to achieve more accurate motion vectors, especially for the where is the vector of ninth-order model parameters and
compressed sequence. The reason is that the interpolated pixels is the vector of 6-spatial neighboring pixels and 3-spatio-tem-
have less artifacts than the LR pixels after the “filter-like” in- poral neighboring pixels of , and is the
terpolation phase. Based on and its motion compensated motion vector of the current block. The interpolation scheme for
frame from , is spatio-temporally interpolated NEDI-9 is shown in Fig. 7(a), where solid circles represent the
using NEDI-9. available pixels and blank circles represent the pixels to be inter-
If the matching block is very different from the current polated. Equation (13) includes one term for the spatial pixels as
blocks, the spatio-temporal pixels should not be used, thus in NEDI-6 and the other term for the spatio-temporal pixels in
preventing the un-related pixels in the previous frame from the previous interpolated frame . The output is edge-di-
contributing to the output. and are combined to rected by the first term and temporal-consistent-directed by the
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6. 404 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 2, FEBRUARY 2010
pling by 2 in both directions. In this simulation, for upsampling
with ratio 1 2 for these methods, only the LR pixels located
in an even row and column (solid circles as plotted in Fig. 5(b)
are used to interpolate the pixels in even columns (square with
lines and circle). The remaining available LR pixels (square)
are ignored. For NEDI-6 and NEDI-9, a window size of 17 17
pixels is chosen for the model parameter estimation. Only 6
HR pixels at the center of teh window are interpolated using
these model parameters. is shifted by (4,4) pixels over the
frame to interpolate all HR pixels. For NEDI-9, for motion
estimation is set to 16 16 and the threshold is experimentally
chosen to be . This helps achieving the highest PSNR
for the interpolated frames of different sequences. A particular
result is shown in Fig. 8 for a zoomed part of a frame of the
Foreman sequence. The PSNR values of the interpolated frames
Fig. 7. Model parameters of 9th order edge-directed interpolation. (a) Interpo-
lation scheme. (b) Parameter estimation. using bicubic, sinc, autoregression, NEDI-6 and NEDI-9 in-
terpolation are 38.86 dB, 38.76 dB, 37.39 dB, 39.31 dB and
39.42 dB, respectively. These results validate the effectiveness
second term. The second term helps reducing the flickering ef-
of NEDI-6 and NEDI-9 for edge-directed interpolation, since
fect of using only frame-based interpolation.
less jaggedness and higher PSNR are attained compared to
The model vector is estimated from its LR model vector
the other methods. Comparing to NEDI-6 using only spatial
, where is shown in Fig. 7(b). In this case, for the spatial
pixels, NEDI-9 using both spatial and spatio-temporal pixels
parameters, the geometric duality is assumed as in NEDI-6.
achieves better visual quality and higher PSNR. When played
This assumption is not needed for the spatio-temporal param-
as a video sequence, interpolated sequence using NEDI-9 also
eters, because all pixels in the previous frame are available.
has less flickering artifacts and a higher quality consistent in
These parameters are finally estimated as in (11) where is
the temporal direction than the single frame-based interpolated
the vector of all mapped LR pixels in
sequence using NEDI-6. Because of the ME part, NEDI-9
and is a matrix whose column is composed
has higher complexity and requires longer running time than
of the 9-spatial and spatio-temporal neighboring pixels of
NEDI-6. For the 2nd frame of Foreman sequence, the running
. The 9-spatial and spatio-temporal neigh-
times are 0.72 s, 0.34 s, 6.59 s, 28.76 s, 433.36 s, and 4690.42
boring pixels of are defined as s for bicubic, sinc, autoregression, NEDI-4, NEDI-6, and
NEDI-9 methods. Note that sinc and autoregression methods
are in C code while the other methods are written using Matlab.
The simulation is run on laptop with Intel 1.83-GHz CPU and
1-GB RAM.
2) Eighth-Order Edge-Directed Interpolation for Up-
sampling With Ratio 2 2: For the proposed NEDI-8, the
comparison is performed with the Shan’s method [19], bicubic,
sinc, and NEDI-4 methods. For NEDI-4 and NEDI-8, the
window size is chosen to be 17 17 and only 4 HR pixels at
the center of are interpolated using these model parameters.
is also shifted by (4,4) pixels over the frame to interpolate
all HR pixels, like in the NEDI-6 and NEDI-9 cases. The frame
V. SIMULATION RESULTS is expanded by reflecting these pixels over the borders in order
to enhance the pixels near the frame borders in the proposed
A. High-Order Edge-Directed Interpolation NEDI-8.
Simulations are performed to compare the proposed PSNR values are shown in Table I for sequences with
high-order edge-directed interpolations with other interpo- different resolutions. To perform a fair comparison to other
lation methods for a wide range of data in different formats. methods that use bilinear interpolation for pixels near the
Both cases of upsampling with ratio of 1 2 and 2 2 are borders, pixels at 5 lines or fewer away from the border are
considered. not counted for the PSNR computation. The Table I shows
1) Sixth-Order and Ninth-Order Edge-Directed Interpola- that NEDI-8 has the highest average PSNR value. The average
tion for Upsampling With Ratio 1 2: The original frames PSNR of NEDI-8 is 3.930 dB, 1.054 dB, 1.198 dB, 0.732 dB
are downsampled by 2 in the horizontal direction (dropping higher than the average PSNR value of Shan’s method, bicubic,
all even columns). The downsized frames are then interpolated sinc, and NEDI-4, respectively.
using bicubic, sinc, autoregression method [8] and the proposed The visual results for a selected part of the Foreman sequence
NEDI-6 and NEDI-9 interpolation. Note that other interpolation are shown in Fig. 9. The result using the sinc-based interpola-
methods, such as [7] and [8], can only be applied for downsam- tion has a lot of jaggedness [Fig. 9(b)]. While the NEDI-4 inter-
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7. VÕ et al.: SELECTIVE DATA PRUNING-BASED COMPRESSION USING HIGH-ORDER EDGE-DIRECTED INTERPOLATION 405
Fig. 8. Comparison of NEDI-6 and NEDI-9 to other methods. (a) Original. (b) Bicubic. (c) Sinc. (d) Autoregression. (e) NEDI-6. (f) NEDI-9.
Fig. 9. Comparison of NEDI-8 to other methods. (a) Original. (b) Sinc. (c) NEDI-4. (d) NEDI-8.
TABLE I
PSNR COMPARISON (IN dB ) size 352 288 to 304 288. An H.264/AVC codec is used to
intra code the frames with . NEDI-6 is used for
the edge-directed interpolation. Each even rows and columns
require one bit to indicate whether it is kept or dropped. Such
as for the frame of size 352 288, a total of
bits is used to indicate the dropped even lines. These bits are
sent as side information in the coded bitstream. For compar-
ison, other data pruning-based methods using sinc, bicubic,
polation has significant less jaggedness, the interpolated frame autoregression, and NEDI-4 interpolation are also given.
in Fig. 9(c) still shows jaggedness along the strong edges. Be- The R-D curves are plotted in Fig. 10(a) and their zoomed
cause NEDI-4 only uses pixels of 2 directions, artifacts can be in parts are plotted in Fig. 10(b). The percentage of bit saving
observed at the intersections of more than 2 edges. On the other between the H264/AVC compression sequence and the NEDI-6
hand, the NEDI-8 interpolated frame in Fig. 9(d) achieves the data pruning-based compression at the same values of is
best quality with least jaggedness. Using pixels in 4 directions, plotted in Fig. 10(c). The result in Fig. 10(a) shows that the data
the NEDI-8 interpolation also has less artifacts at the intersec- pruning-based compression using NEDI-6 is better than data
tion of more than 2 edges. With respect to objective quality, the pruning-based compression using sinc, bicubic, autoregression
proposed NEDI-8 has the highest PSNR values for all the se- and NEDI-4 methods. The data pruning-based compression
quences across different resolutions. Because of the extra round achieves a better R-D than H264/AVC in the range 31–41 dB.
in the proposed NEDI-8, its running time is longer than NEDI-4. In this range, at the same bit-rate, the PSNR value of data
For Foreman image, the running times are 0.45 s, 0.13 s, 11.56 pruning-based compression is about 0.3–0.5 dB higher than
s, and 65.90 s for bicubic, sinc, NEDI-4 and NEDI-8 methods. the PSNR value of H.264/AVC compression. At the same
Note that sinc method is in C code while the other methods are PSNR, the data pruning-based compression saves about 5% of
written using Matlab software. bit-rate comparing to bit-rate of the H.264/AVC compression.
As shown in Fig. 10(c), at the same QI, the percentage of bit
B. Data Pruning-Based Compression saving is about 4.2%–6.6%. The reconstructed frames using
1) Single-Frame Data Pruning-Based Compression: The data pruning-based compression with sinc, bicubic, autoregres-
simulation in this section verifies the validity of the data sion, NEDI-4 and NEDI-6 methods are shown in Fig. 11(b)–(f)
pruning-based compression method for single frames. This and their zoomed in part are shown in Fig. 12(b)–(f). The data
data pruning-based compression is applied to the compression pruned frames are compressed with and the corre-
of images or intra frames. The target is set to 50 dB. sponding bit-rate is 1.36 Mbps. The PSNR of the reconstructed
Subsequently, the algorithm prunes the frames of Foreman of frame using data pruning-based compression with sinc, bicubic,
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8. 406 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 2, FEBRUARY 2010
Fig. 10. Comparison results for R-D curves of single frame data pruning-based compression. (a) Whole R-D curves. (b) One zoomed in part of (a). (c) Percentage
of bit saving.
Fig. 11. Comparison of NEDI-6 to other interpolation methods in case of single frame data pruning-based compression. (a) Original. (b) Sinc. (c) Bicubic.
(d) Autoregression (37.78 dB). (e) NEDI-4. (f) NEDI-6.
autoregression, NEDI-4 and NEDI-6 methods are 37.79 dB, 2) Multiframe Data Pruning-Based Compression: The data
37.80 dB, 37.78 dB, 37.42 dB, and 37.91 dB, respectively. pruning approach is applied to video compression. An experi-
The results show that the reconstructed frame using NEDI-6 ment is performed in which a GOP of 15 frames of Akiyo is
in Fig. 11 has less artifacts than other methods. Because the pruned with the target dB. Three downsam-
reconstructed frames using autoregression and NEDI-4 are not pling scenarios of dropping all even rows, all even columns all
based on the LR pixels located at even rows, the HR pixels are even rows and columns then using the interpolation scenarios
not consistent to each other and cause some artifacts at the teeth of factors of 1 2, 2 1 and 2 2 are considered to determine
areas in Fig. 11(d) and (e). the best number of lines to be dropped. Simulation shows that
An additional simulation is performed to analysis the affect dropping 160 columns and keepping all rows are the best so-
of the target PSNR on the pruned frame size and the R-D curve lution which achieves the most dropped pixels while still keeps
of the data pruning-based compression. The results in Table II the PSNR of reconstructed frame higher than 45 dB. As a conse-
show that when the target PSNR decreases, more data is con- quence, the frame size is reduced from 720 480 to 320 480
sidered to be dropped while the PSNR range having better R-D lines. An H.264 codec is applied with the GOP structure
curve reduces. The best case to get highest average PSNR im- and . The is averaged over the
provement of dB is obtained when the target PSNR is set to whole GOP, so that the same lines are dropped for all the frames.
50 dB. The table also shows that the compressed bitrate saving In this way, the side information to determine the dropped lines
increases when the target PSNR decreases. is greatly reduced. The extra bit-rate is 1.2 Kbps for the whole
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9. VÕ et al.: SELECTIVE DATA PRUNING-BASED COMPRESSION USING HIGH-ORDER EDGE-DIRECTED INTERPOLATION 407
Fig. 12. One zoomed in part of Fig. 11. (a) Original. (b) Sinc. (c) Bicubic. (d) Autoregression. (e) NEDI-4. (f) NEDI-6.
TABLE II of 37.83 dB and 37.91 dB respectively for the H.264/AVC and
PSNR COMPARISON (IN dB ) the proposed data pruning-based compressed sequences. Re-
sults show that the proposed data pruning-based compressed
frame in Fig. 14(b) has higher visual quality and less artifacts
than the H.264/AVC compressed frame in Fig. 14(a). This merit
can be explained by the interpolation phase, which helps re-
ducing the blocking and ringing artifacts, and the smaller quan-
tization step level. Because of the ’filter-like’ interpolation, the
reconstructed sequence in the low bit-rate has fewer blocking
GOP, which again is very small compared to the total bit-rate of artifact than the direct compressed sequence with high compres-
the compressed bitstream. For interpolation, single frame-based sion level.
NEDI-6 is used for the first I frame while multiframe-based Both PSNR curve and visual results validate the effectiveness
NEDI-9 is employed for the following frames. For comparison, of the proposed data pruning-based compression. The proposed
the data pruning scheme is applied to the sequence down- and algorithm requires an interpolation step in the data pruning and
up-sized by 2 2 with the uniform sinc interpolation. reconstruction phases, so the complexity of data pruning-based
The R-D curves are shown in Fig. 13(a), while Fig. 13(b) are compression is higher than the normal compression. However,
zoomed in parts. These results show that the R-D curve of the the coding and decoding time of the proposed method decreases
sinc data-pruned method is consistently below the curve of the proportionally to the size reduction of the data pruned frame.
optimal data pruning method. The proposed method is better in Such as for case of data pruning from the original frame size
the range 32–37.5 dB compared to H.264/AVC. The PSNR im- of 720 480 to 320 480, both encoding and decoding time
provement at the same bit-rate is around 0.3–0.7 dB in the range. for data pruned sequence is only 50% of the encoding and de-
As shown in Fig. 13(c), the percentage of bit-rate saving of the coding time for the original sequence. Additional simulations
optimal data pruning-based compressed sequence is 23%–36% show that to further reduce the running time in the encoding
compared to the H.264/AVC using the same quantization step phase, a simple interpolator such as bilinear interpolator can be
size. Even having the same bit-rate and PSNR values, the recon- applied at the data pruning phase in Fig. 2 while still nearly
structed frames have less artifacts because they are compressed keeps the same performance when using high-order edge-di-
with smaller quantization step and . Fig. 14 shows the rected interpolators. For structure , the same data pruning
comparison between the H.264/AVC compressed frame and the phase for structure can be applied without any modifi-
optimal data pruning-based compressed frame at the quanti- cation. The B frames require smaller number of bits for com-
zation level of 35 and 32, respectively. These sequences have pression and the extra bits for indicating the dropped lines be-
nearly same bit-rate of 92 Kbps and 94 Kbps and same PSNR come significant comparing to the bit for coding frame. So
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10. 408 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 2, FEBRUARY 2010
Fig. 13. Comparison results for multiframe data pruning-based compression. (a) R-D curves. (b) Zoom in of 13 (a). (c) Percentage of bit saving.
Fig. 14. Comparison for H.264/AVC compression and optimal data pruning-based compression with same bit-rate and PSNR values. (a) H.264/AVC. (b) Optimal
data pruning-based.
medium compression level. The NEDI-6 and NEDI-9 for up-
sampling only rows can be also applied for de-interleaving. For
the same sequence, the R-D performance for single frame data
pruning-based compression is much better than the R-D perfor-
mance of multifame data pruning-based compression. This is
because with the same target PSNR, higher percentage of data
can be dropped for a single image than video sequence. Another
reason is that the same rows/columns are dropped over frames
in the GOP and more bits are required to compress the objects
Fig. 15. Comparison for H.264/AVC compression and optimal data pruning- moving over the dropped lines.
based compression with same bit-rate and PSNR values. (a) H.264/AVC. In future work, the location of the dropped lines should be
(b) Optimal data pruning-based. adaptive to the motion of the moving objects. Instead of using
only the pixels at odd indices, high-order edge-directed inter-
polation methods may use more available pixels to estimate
the R-D improvement using structure is better than the more accurately the model parameters. Additionally, the objec-
R-D improvement using structure . All simulation results tive function of the data pruning algorithm may be extended to
can be found at http://videoprocessing.ucsd.edu/~dungvo/dat- consider the coding efficiency of dropping these pixels to fur-
aprune.html. ther improve the R-D curve. A more efficient data pruning-based
compression for dropping the whole frame can also be consid-
VI. CONCLUSION ered using MCFI methods for video sequences with fast mo-
tions.
The paper proposed a novel data pruning-based compression
method to reduce the bit-rate. High-order edge-directed inter- ACKNOWLEDGMENT
polations using more surrounding pixels are also discussed to The authors would like to thank Y. Zheng for the interesting
adapt to different data pruning schemes. The results show that discussions at Thomson Corporate Research.
these high-order edge-directed interpolation methods help re-
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[10] N. Mueller, Y. Lu, and M. N. Do, “Image interpolation using multi- She is currently a senior member of the tech-
scale geometric representations,” in SPIE Conf. Electronic Imaging, nical staff at Corporate Research, Thomson, Inc.,
Feb. 2007, vol. 6498. Princeton, NJ. Her current research interest is mainly
[11] N. Mueller and T. Q. Nguyen, “Image interpolation using classification on image and video compression. Her previous
and stitching,” presented at the IEEE Conf. Image Process., Oct. 2008. research is on video transcoding, error conceal-
[12] S. C. P. I. K. Tam, “Effective color interpolation in CCD color filter ment, and data hiding. She is actively involved in
arrays using signal correlation,” IEEE Trans. Circuits Syst. Video JVT/MPEG standardization process.
Technol., vol. 13, no. 3, pp. 503–513, Jun. 2003. Dr. Yin received the IEEE Circuits and Systems Society Best Paper Award for
[13] D. Menon, S. Andriani, and G. Calvagno, “Demosaicing with direc- her article in the IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO
tional filtering and a posteriori decision,” IEEE Trans. Image Process., TECHNOLOGY in 2003.
vol. 16, no. 1, pp. 132–141, Jan. 2007.
[14] X. Li, “Demosaicing by successive approximation,” IEEE Trans. Image
Process., vol. 14, no. 3, pp. 370–379, Mar. 2005.
[15] G. Shen, B. Zeng, Y.-Q. Zhang, and M. L. Liou, “Transcoder with Cristina Gomila (M’01) received the M.S. degree in
arbitrarily resizing capability,” in Proc. IEEE Int. Symposium Circuits telecommunication engineering from the Technical
Syst., May 2001, vol. 5, pp. 22–28. University of Catalonia, Spain, in 1997, and the Ph.D.
[16] B. Choi, J. Han, C. Kim, and S. Ko, “Motion-compensated frame in- degree from the Ecole des Mines de Paris, France, in
terpolation using bilateral motion estimation and adaptive overlapped 2001.
block motion compensation,” IEEE Trans. Image Process., vol. 17, no. She then joined Thomson, Inc., Corporate Re-
4, pp. 407–416, Apr. 2007. search Princeton, Princeton, NJ. She was a core
[17] A. Huang and T. Nguyen, “A multistage motion vector processing member in the development of Thomson’s Film
method for motion-compensated frame interpolation,” IEEE Trans. Grain Technology and actively contributed to several
Image Process., vol. 17, no. 5, pp. 694–708, May 2008. MPEG standardization efforts, including AVC and
[18] S. G. Mallat, A Wavelet Tour of Signal Processing. New York: Aca- MVC. Since 2005, she has managed the Compres-
demic, 1998. sion Research Group at Thomson CR Princeton. Her current research interests
[19] Q. Shan, Z. Li, J. Jia, and C. Tang, “Fast image/video upsampling,” focus on advanced video coding for professional applications.
ACM Transactions on Graphics (SIGGRAPH ASIA 2008), vol. 27,
2008.
Truong Q. Nguyen (F’06) is currently a Professor at
the ECE Department, University of California at San
Diego, La Jolla. He is the coauthor (with Prof. G.
Strang) of the popular textbook Wavelets and Filter
˜
Dung T. Võ (S’06–M’09) received the B.S. and M.S. Banks (Wellesley-Cambridge Press, 1997) and the
degrees from Ho Chi Minh City University of Tech- author of several Matlab-based toolboxes on image
nology, Vietnam, in 2002 and 2004, respectively, and compression, electrocardiogram compression, and
the Ph.D. degree from the University of California at filter bank design. He has over 200 publications. His
San Diego, La Jolla, in 2009. research interests are video processing algorithms
He has been a Fellow of the Vietnam Education and their efficient implementation.
Foundation (VEF) since 2005 and has been on the Prof. Nguyen received the IEEE TRANSACTIONS
teaching staff of Ho Chi Minh City University of ON SIGNAL PROCESSING Paper Award (Image and Multidimensional Pro-
Technology since 2002. He interned at Mitsubishi cessing area) for the paper he co-wrote with Prof. P. P. Vaidyanathan on
Electric Research Laboratories (MERL), Cambridge, linear-phase perfect-reconstruction filter banks (1992). He received the NSF
MA, and Thomson Corporate Research, Princeton, Career Award in 1995 and is currently the Series Editor (Digital Signal
NJ, in the summers of 2007 and 2008, respectively. He has been a senior Processing) for Academic Press. He served as Associate Editor for the IEEE
research engineer at the Digital Media Solutions Lab, Samsung Information TRANSACTIONS ON SIGNAL PROCESSING (1994–1996), the IEEE SIGNAL
Systems America (Samsung US R&D Center), Irvine, CA, since 2009. His PROCESSING LETTERS (2001–2003), the IEEE TRANSACTIONS ON CIRCUITS
research interests are algorithms and applications for image and video coding AND SYSTEMS (1996–1997, 2001–2004), and the IEEE TRANSACTIONS ON
and postprocessing. IMAGE PROCESSING (2004–2005).
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