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.
Low Memory Low Complexity Image Compression Using HSSPIHT EncoderIJERA Editor
Due to the large requirement for memory and the high complexity of computation, JPEG2000 cannot be used in
many conditions especially in the memory constraint equipment. The line-based W avelet transform was
proposed and accepted because lower memory is required without affecting the result of W avelet transform, In
this paper, the improved lifting schem e is introduced to perform W avelet transform to replace Mallat method
that is used in the original line-based wavelet transform. In this a three-adder unit is adopted to realize lifting
scheme. It can perform wavelet transform with less computation and reduce memory than Mallat algorithm. The
corresponding HS_SPIHT coding is designed here so that the proposed algorithm is more suitable for
equipment. W e proposed a highly scale image compression scheme based on the Set Partitioning in
Hierarchical Trees (SPIHT) algorithm. Our algorithm, called Highly Scalable SPIHT (HS_SPIHT), supports
High Compression efficiency, spatial and SNR scalability and provides l bit stream that can be easily adapted to
give bandwidth and resolution requirements by a simple transcoder (parse). The HS_SPIHT algorithm adds
the spatial scalability feature without sacrificing the S NR embeddedness property as found in the original
SPIHT bit stream. Highly scalable image compression scheme based on the SPIHT algorithm the proposed
algorithm used, highly scalable SPIHT (HS_SPIHT) Algorithm, adds the spatial scalability feature to the SPIHT
algorithm through the introduction of multiple resolution dependent lists and a resolution-dependent sorting
pass. SPIHT keeps the import features of the original SPIHT algorithm such as compression efficiency, full
SNR Scalability and low complexity.
Dynamic Texture Coding using Modified Haar Wavelet with CUDAIJERA Editor
Texture is an image having repetition of patterns. There are two types, static and dynamic texture. Static texture is an image having repetitions of patterns in the spatial domain. Dynamic texture is number of frames having repetitions in spatial and temporal domain. This paper introduces a novel method for dynamic texture coding to achieve higher compression ratio of dynamic texture using 2D-modified Haar wavelet transform. The dynamic texture video contains high redundant parts in spatial and temporal domain. Redundant parts can be removed to achieve high compression ratios with better visual quality. The modified Haar wavelet is used to exploit spatial and temporal correlations amongst the pixels. The YCbCr color model is used to exploit chromatic components as HVS is less sensitive to chrominance. To decrease the time complexity of algorithm parallel programming is done using CUDA (Compute Unified Device Architecture). GPU contains the number of cores as compared to CPU, which is utilized to reduce the time complexity of algorithms.
Secured Data Transmission Using Video Steganographic SchemeIJERA Editor
Steganography is the art of hiding information in ways that avert the revealing of hiding messages. Video Steganography is focused on spatial and transform domain. Spatial domain algorithm directly embedded information in the cover image with no visual changes. This kind of algorithms has the advantage in Steganography capacity, but the disadvantage is weak robustness. Transform domain algorithm is embedding the secret information in the transform space. This kind of algorithms has the advantage of good stability, but the disadvantage of small capacity. These kinds of algorithms are vulnerable to steganalysis. This paper proposes a new Compressed Video Steganographic scheme. The data is hidden in the horizontal and the vertical components of the motion vectors. The PSNR value is calculated so that the quality of the video after the data hiding is evaluated.
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
This document provides an overview of a research project on image compression. It discusses image compression techniques including lossy and lossless compression. It describes using discrete wavelet transform, lifting wavelet transform, and stationary wavelet transform for image transformation. Experiments were conducted to compare the compression ratio and processing time of different combinations of wavelet transforms, vector quantization, and Huffman/Arithmetic coding. The results were analyzed to evaluate the compression performance and efficiency of the different methods.
Comparative Analysis of Lossless Image Compression Based On Row By Row Classi...IJERA Editor
This document proposes and evaluates a near lossless image compression algorithm that divides color images into red, green, and blue channels. It classifies pixels in each channel row-by-row and records the results in mask images. The image data is then decomposed into sequences based on the classification and the mask images are hidden in the least significant bits of the sequences. Different encoding schemes like LZW, Huffman, and RLE are applied and compared. Experimental results on test images show the proposed algorithm achieves smaller bits per pixel than simple encoding schemes. PSNR values also indicate very little difference between original and reconstructed images.
High Speed and Area Efficient 2D DWT Processor Based Image Compressionsipij
The document describes a proposed high speed and area efficient 2D discrete wavelet transform (DWT) processor design for image compression applications implemented on FPGAs. The design uses a pipelined partially serial architecture to enhance speed while optimally utilizing FPGA resources. Simulation results show the design operating at 231MHz on a Spartan 3 FPGA, a 15% improvement over alternative designs. Resource utilization and speed are improved compared to previous implementations through the optimized DWT processor architecture and FPGA platform choice.
This document proposes a new shape-adaptive reversible integer lapped transform (SA-RLT) method for region-of-interest (ROI) coding of 2D remote sensing images. SA-RLT performs better than other transforms like SA-DWT and SA-DCT. The method segments the ROI using a new algorithm rather than hand segmentation. It then designs a SA-RLT based ROI compression scheme using object-based set partitioned embedded block coding (OBSPECK). Experimental results show that SA-RLT compression outperforms DCT and DWT compression for remote sensing images. The method provides flexible bit rate control and allows lossless ROI coding without background areas.
Low Memory Low Complexity Image Compression Using HSSPIHT EncoderIJERA Editor
Due to the large requirement for memory and the high complexity of computation, JPEG2000 cannot be used in
many conditions especially in the memory constraint equipment. The line-based W avelet transform was
proposed and accepted because lower memory is required without affecting the result of W avelet transform, In
this paper, the improved lifting schem e is introduced to perform W avelet transform to replace Mallat method
that is used in the original line-based wavelet transform. In this a three-adder unit is adopted to realize lifting
scheme. It can perform wavelet transform with less computation and reduce memory than Mallat algorithm. The
corresponding HS_SPIHT coding is designed here so that the proposed algorithm is more suitable for
equipment. W e proposed a highly scale image compression scheme based on the Set Partitioning in
Hierarchical Trees (SPIHT) algorithm. Our algorithm, called Highly Scalable SPIHT (HS_SPIHT), supports
High Compression efficiency, spatial and SNR scalability and provides l bit stream that can be easily adapted to
give bandwidth and resolution requirements by a simple transcoder (parse). The HS_SPIHT algorithm adds
the spatial scalability feature without sacrificing the S NR embeddedness property as found in the original
SPIHT bit stream. Highly scalable image compression scheme based on the SPIHT algorithm the proposed
algorithm used, highly scalable SPIHT (HS_SPIHT) Algorithm, adds the spatial scalability feature to the SPIHT
algorithm through the introduction of multiple resolution dependent lists and a resolution-dependent sorting
pass. SPIHT keeps the import features of the original SPIHT algorithm such as compression efficiency, full
SNR Scalability and low complexity.
Dynamic Texture Coding using Modified Haar Wavelet with CUDAIJERA Editor
Texture is an image having repetition of patterns. There are two types, static and dynamic texture. Static texture is an image having repetitions of patterns in the spatial domain. Dynamic texture is number of frames having repetitions in spatial and temporal domain. This paper introduces a novel method for dynamic texture coding to achieve higher compression ratio of dynamic texture using 2D-modified Haar wavelet transform. The dynamic texture video contains high redundant parts in spatial and temporal domain. Redundant parts can be removed to achieve high compression ratios with better visual quality. The modified Haar wavelet is used to exploit spatial and temporal correlations amongst the pixels. The YCbCr color model is used to exploit chromatic components as HVS is less sensitive to chrominance. To decrease the time complexity of algorithm parallel programming is done using CUDA (Compute Unified Device Architecture). GPU contains the number of cores as compared to CPU, which is utilized to reduce the time complexity of algorithms.
Secured Data Transmission Using Video Steganographic SchemeIJERA Editor
Steganography is the art of hiding information in ways that avert the revealing of hiding messages. Video Steganography is focused on spatial and transform domain. Spatial domain algorithm directly embedded information in the cover image with no visual changes. This kind of algorithms has the advantage in Steganography capacity, but the disadvantage is weak robustness. Transform domain algorithm is embedding the secret information in the transform space. This kind of algorithms has the advantage of good stability, but the disadvantage of small capacity. These kinds of algorithms are vulnerable to steganalysis. This paper proposes a new Compressed Video Steganographic scheme. The data is hidden in the horizontal and the vertical components of the motion vectors. The PSNR value is calculated so that the quality of the video after the data hiding is evaluated.
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
This document provides an overview of a research project on image compression. It discusses image compression techniques including lossy and lossless compression. It describes using discrete wavelet transform, lifting wavelet transform, and stationary wavelet transform for image transformation. Experiments were conducted to compare the compression ratio and processing time of different combinations of wavelet transforms, vector quantization, and Huffman/Arithmetic coding. The results were analyzed to evaluate the compression performance and efficiency of the different methods.
Comparative Analysis of Lossless Image Compression Based On Row By Row Classi...IJERA Editor
This document proposes and evaluates a near lossless image compression algorithm that divides color images into red, green, and blue channels. It classifies pixels in each channel row-by-row and records the results in mask images. The image data is then decomposed into sequences based on the classification and the mask images are hidden in the least significant bits of the sequences. Different encoding schemes like LZW, Huffman, and RLE are applied and compared. Experimental results on test images show the proposed algorithm achieves smaller bits per pixel than simple encoding schemes. PSNR values also indicate very little difference between original and reconstructed images.
High Speed and Area Efficient 2D DWT Processor Based Image Compressionsipij
The document describes a proposed high speed and area efficient 2D discrete wavelet transform (DWT) processor design for image compression applications implemented on FPGAs. The design uses a pipelined partially serial architecture to enhance speed while optimally utilizing FPGA resources. Simulation results show the design operating at 231MHz on a Spartan 3 FPGA, a 15% improvement over alternative designs. Resource utilization and speed are improved compared to previous implementations through the optimized DWT processor architecture and FPGA platform choice.
This document proposes a new shape-adaptive reversible integer lapped transform (SA-RLT) method for region-of-interest (ROI) coding of 2D remote sensing images. SA-RLT performs better than other transforms like SA-DWT and SA-DCT. The method segments the ROI using a new algorithm rather than hand segmentation. It then designs a SA-RLT based ROI compression scheme using object-based set partitioned embedded block coding (OBSPECK). Experimental results show that SA-RLT compression outperforms DCT and DWT compression for remote sensing images. The method provides flexible bit rate control and allows lossless ROI coding without background areas.
Image compression using Hybrid wavelet Transform and their Performance Compa...IJMER
Images may be worth a thousand words, but they generally occupy much more space in hard disk, or
bandwidth in a transmission system, than their proverbial counterpart. Compressing an image is significantly
different than compressing raw binary data. Of course, general purpose compression programs can be used to
compress images, but the result is less than optimal. This is because images have certain statistical properties
which can be exploited by encoders specifically designed for them. Also, some of the finer details in the image
can be sacrificed for the sake of saving a little more bandwidth or storage space. Compression is the process of
representing information in a compact form. Compression is a necessary and essential method for creating
image files with manageable and transmittable sizes. The data compression schemes can be divided into
lossless and lossy compression. In lossless compression, reconstructed image is exactly same as compressed
image. In lossy image compression, high compression ratio is achieved at the cost of some error in reconstructed
image. Lossy compression generally provides much higher compression than lossless compression.
Implementation of Vedic Multiplier in Image Compression Using Discrete Wavele...IJSRD
Fast Multiplication is one of the most momentous parts in any processor speed which progresses the speed of the manoeuvre like in exceptional application processor like Digital signal processor (DSPs). In this paper Implementation of Vedic Multiplier in Image Compression using DWT Algorithm is being in attendance. The DWT is used to crumble the image into different group of images and the research work in this paper represents the effectiveness of Urdhva Triyagbhyam Vedic Method in Image firmness for burgeoning which smacks a difference in authentic process of multiplication itself.A novelVedic multiplier with less number of half adders and Full Addersis proposed in order to overcome such an error. Simulation is done in Matlab2008a and Modelsim10.0b.Synthesis and Implementation is performed by Xilinx 14.
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.
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.
A Novel Algorithm for Watermarking and Image Encryption cscpconf
Digital watermarking is a method of copyright protection of audio, images, video and text. We
propose a new robust watermarking technique based on contourlet transform and singular value
decomposition. The paper also proposes a novel encryption algorithm to store a signed double
matrix as an RGB image. The entropy of the watermarked image and correlation coefficient of
extracted watermark image is very close to ideal values, proving the correctness of proposed
algorithm. Also experimental results show resiliency of the scheme against large blurring attack
like mean and gaussian filtering, linear filtering (high pass and low pass filtering) , non-linear
filtering (median filtering), addition of a constant offset to the pixel values and local exchange of pixels .Thus proving the security, effectiveness and robustness of the proposed watermarking algorithm.
Survey paper on image compression techniquesIRJET Journal
This document summarizes and compares several popular image compression techniques: wavelet compression, JPEG/DCT compression, vector quantization (VQ), fractal compression, and genetic algorithm compression. It finds that all techniques perform satisfactorily at 0.5 bits per pixel, but for very low bit rates like 0.25 bpp, wavelet compression techniques like EZW perform best in terms of compression ratio and quality. Specifically, EZW and JPEG are more practical than others at low bit rates. The document also notes advantages and disadvantages of each technique and concludes hybrid approaches may achieve even higher compression ratios while maintaining image quality.
Color image analyses using four deferent transformationsAlexander Decker
This document discusses and compares four different image transformations: discrete Fourier transform (DFT), discrete cosine transform (DCT), wavelet transform (DWT), and discrete multiwavelet transform (DMWT). It analyzes the effectiveness of each transform for processing color images in terms of noise reduction, enhancement, brightness, compression, and resolution. The performance of the techniques is evaluated using computer simulations in Visual Basic 6.
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.
Satellite image contrast enhancement using discrete wavelet transformHarishwar Reddy
This document discusses contrast enhancement of satellite images using discrete wavelet transform and singular value decomposition. It provides background on contrast and techniques like histogram equalization. It then describes discrete wavelet transform and singular value decomposition, their applications, advantages, and uses. The document concludes that a new technique was proposed combining DWT and SVD for image equalization, which showed better results than conventional techniques in experiments.
Transform coding is used in image and video processing to exploit correlations between neighboring pixels. The discrete cosine transform (DCT) is commonly used as it provides good energy compaction and de-correlation. DCT represents data as a sum of variable frequency cosine waves in the frequency domain. It has properties like separability and orthogonality that make it efficient for computation. DCT exhibits excellent energy compaction and removal of redundancy between pixels, making it useful for image and video compression.
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.
This document describes a hybrid technique for image enhancement that uses both frequency domain and spatial domain techniques. It begins with applying frequency domain techniques like discrete cosine transform (DCT) or discrete wavelet transform (DWT) to separate an image into magnitude and phase spectra. The magnitude is then enhanced before recombining it with the phase using inverse DCT/DWT. Spatial domain techniques like power law or log transforms are then applied to further enhance contrast and brightness. The technique is evaluated on sample images and shown to achieve better PSNR and lower MSE than frequency domain techniques alone. In conclusion, combining frequency and spatial domain methods provides an effective approach for image enhancement.
This document discusses techniques for effective compression of digital video. It introduces several key algorithms used in video compression, including discrete cosine transform (DCT) for spatial redundancy reduction, motion estimation (ME) for temporal redundancy reduction, and embedded zerotree wavelet (EZW) transforms. DCT is used to compress individual video frames by removing spatial correlations within frames. Motion estimation compares blocks of pixels between frames to find and encode motion vectors rather than full pixel values, reducing file size. Combined, these techniques can achieve high compression ratios while maintaining high video quality for storage and transmission.
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
This document discusses a proposed method for lossless image compression that uses a hybrid of wavelet transforms, embedded zero tree coding, and Huffman coding. Specifically, it uses discrete wavelet transforms to decompose images into sub-bands, applies embedded zero tree coding to the wavelet coefficients to encode insignificant coefficients as zero trees, and then uses Huffman coding for further compression. Tables of results are presented showing compression ratios, bits per pixel, and PSNR values for images compressed with different threshold values in the wavelet decomposition. The goal is to determine an optimal threshold level for each decomposition to maximize compression while perfectly reconstructing the original image content.
This document summarizes a student project on implementing lossless discrete wavelet transform (DWT) and inverse discrete wavelet transform (IDWT). It provides an overview of the project, which includes introducing DWT, reviewing literature on lifting schemes for faster DWT computation, and simulating a 2D (5,3) DWT. The results show DWT blocks decomposing signals into high and low pass coefficients. Applications mentioned are in medical imaging, signal denoising, data compression and image processing. The conclusion discusses the need for lossless transforms in medical imaging. Future work could extend this to higher level transforms and applications like compression and watermarking.
This document discusses an enhanced technique for secure and reliable watermarking using Modified Haar Wavelet Transform (MFHWT). The proposed technique embeds a watermark into an original image using discrete wavelet transform (DWT) and wavelet packet transform (WPT) according to the size of the watermark. MFHWT is a memory efficient, fast, and simple transform. The watermarking process involves embedding and extraction processes. Various watermarking techniques in different transform domains are discussed, including DWT and WPT. The proposed algorithm uses MFHWT for decomposition and reconstruction. Image quality is measured using metrics like MSE and PSNR, with higher PSNR indicating better quality. The technique achieves robustness
Design and implementation of image compression using set partitioning in hier...eSAT Journals
Abstract
To store digital image and video in raw form require large amount of memory space. Image compression means reducing the size of image file without degrading quality of image. Depending on the reconstructed image to be exactly same as the original or some unknown loss may incurred image compression divided into two techniques lossy and lossless techniques. In this paper we present hybrid model which is the combination of several compression techniques. This paper present DWT, DCT and SPIHT implementation. Simulation has been carried out on different images like Lena , Barbra, Cameraman, Test drive . Result analysis is done through parameters like MSE, PSNR and Elapsed time. Values of this parameters will go better than the old algorithms because here we apply SPIHT lossless technique and also due to hybrid combination of DWT and DCT we will get good level of compression. The result analysis shows that the proposed hybrid algorithm performs much better in terms of PSNR with a higher compression ratio as compared to standalone DWT and DCT techniques.
Keywords- DWT, DCT, SPIHT, PSNR, MSE
Improvement in Traditional Set Partitioning in Hierarchical Trees (SPIHT) Alg...AM Publications
In this paper, an improved SPIHT algorithm based on Huffman coding and discrete wavelet transform for
image compression has been developed. The traditional SPIHT algorithm application is limited in terms of PSNR and
Compression Ratio. The improved SPIHT algorithm gives better performance as compared with traditional SPIHT
algorithm; here we used additional Huffman encoder along with SPIHT encoder. Huffman coding is an entropy
encoding algorithm uses a specific method for choosing the representation for each symbol, resulting in a prefix code.
The input gray scale image is decomposed by 'bior4.4' wavelet and wavelet coefficients are indexed and scanned by
DWT, then applied to encode the coefficient by SPIHT encoder followed by Huffman encoder which gives the
compressed image. At receiver side the decoding process is applied by Huffman decoder followed by SPIHT decoder.
The Reconstructed gray scale image looks similar to the applied gray scale image.
A novel technique in spiht for medical image compressionIAEME Publication
This document presents a novel technique for medical image compression using multi-wavelet transformation with modified fast Haar wavelet transform (MFHWT) in the Set Partitioning in Hierarchical Trees (SPIHT) algorithm. The technique aims to minimize information loss and increase image quality during compression. It involves applying multi-wavelet decomposition using MFHWT on the image, then using SPIHT to find significant and insignificant pixels. A region of interest is selected before compression to avoid loss of important image details. The technique is evaluated on medical images and shows improved peak signal-to-noise ratio and compression ratio compared to standard SPIHT.
Image compression using Hybrid wavelet Transform and their Performance Compa...IJMER
Images may be worth a thousand words, but they generally occupy much more space in hard disk, or
bandwidth in a transmission system, than their proverbial counterpart. Compressing an image is significantly
different than compressing raw binary data. Of course, general purpose compression programs can be used to
compress images, but the result is less than optimal. This is because images have certain statistical properties
which can be exploited by encoders specifically designed for them. Also, some of the finer details in the image
can be sacrificed for the sake of saving a little more bandwidth or storage space. Compression is the process of
representing information in a compact form. Compression is a necessary and essential method for creating
image files with manageable and transmittable sizes. The data compression schemes can be divided into
lossless and lossy compression. In lossless compression, reconstructed image is exactly same as compressed
image. In lossy image compression, high compression ratio is achieved at the cost of some error in reconstructed
image. Lossy compression generally provides much higher compression than lossless compression.
Implementation of Vedic Multiplier in Image Compression Using Discrete Wavele...IJSRD
Fast Multiplication is one of the most momentous parts in any processor speed which progresses the speed of the manoeuvre like in exceptional application processor like Digital signal processor (DSPs). In this paper Implementation of Vedic Multiplier in Image Compression using DWT Algorithm is being in attendance. The DWT is used to crumble the image into different group of images and the research work in this paper represents the effectiveness of Urdhva Triyagbhyam Vedic Method in Image firmness for burgeoning which smacks a difference in authentic process of multiplication itself.A novelVedic multiplier with less number of half adders and Full Addersis proposed in order to overcome such an error. Simulation is done in Matlab2008a and Modelsim10.0b.Synthesis and Implementation is performed by Xilinx 14.
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.
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.
A Novel Algorithm for Watermarking and Image Encryption cscpconf
Digital watermarking is a method of copyright protection of audio, images, video and text. We
propose a new robust watermarking technique based on contourlet transform and singular value
decomposition. The paper also proposes a novel encryption algorithm to store a signed double
matrix as an RGB image. The entropy of the watermarked image and correlation coefficient of
extracted watermark image is very close to ideal values, proving the correctness of proposed
algorithm. Also experimental results show resiliency of the scheme against large blurring attack
like mean and gaussian filtering, linear filtering (high pass and low pass filtering) , non-linear
filtering (median filtering), addition of a constant offset to the pixel values and local exchange of pixels .Thus proving the security, effectiveness and robustness of the proposed watermarking algorithm.
Survey paper on image compression techniquesIRJET Journal
This document summarizes and compares several popular image compression techniques: wavelet compression, JPEG/DCT compression, vector quantization (VQ), fractal compression, and genetic algorithm compression. It finds that all techniques perform satisfactorily at 0.5 bits per pixel, but for very low bit rates like 0.25 bpp, wavelet compression techniques like EZW perform best in terms of compression ratio and quality. Specifically, EZW and JPEG are more practical than others at low bit rates. The document also notes advantages and disadvantages of each technique and concludes hybrid approaches may achieve even higher compression ratios while maintaining image quality.
Color image analyses using four deferent transformationsAlexander Decker
This document discusses and compares four different image transformations: discrete Fourier transform (DFT), discrete cosine transform (DCT), wavelet transform (DWT), and discrete multiwavelet transform (DMWT). It analyzes the effectiveness of each transform for processing color images in terms of noise reduction, enhancement, brightness, compression, and resolution. The performance of the techniques is evaluated using computer simulations in Visual Basic 6.
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.
Satellite image contrast enhancement using discrete wavelet transformHarishwar Reddy
This document discusses contrast enhancement of satellite images using discrete wavelet transform and singular value decomposition. It provides background on contrast and techniques like histogram equalization. It then describes discrete wavelet transform and singular value decomposition, their applications, advantages, and uses. The document concludes that a new technique was proposed combining DWT and SVD for image equalization, which showed better results than conventional techniques in experiments.
Transform coding is used in image and video processing to exploit correlations between neighboring pixels. The discrete cosine transform (DCT) is commonly used as it provides good energy compaction and de-correlation. DCT represents data as a sum of variable frequency cosine waves in the frequency domain. It has properties like separability and orthogonality that make it efficient for computation. DCT exhibits excellent energy compaction and removal of redundancy between pixels, making it useful for image and video compression.
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.
This document describes a hybrid technique for image enhancement that uses both frequency domain and spatial domain techniques. It begins with applying frequency domain techniques like discrete cosine transform (DCT) or discrete wavelet transform (DWT) to separate an image into magnitude and phase spectra. The magnitude is then enhanced before recombining it with the phase using inverse DCT/DWT. Spatial domain techniques like power law or log transforms are then applied to further enhance contrast and brightness. The technique is evaluated on sample images and shown to achieve better PSNR and lower MSE than frequency domain techniques alone. In conclusion, combining frequency and spatial domain methods provides an effective approach for image enhancement.
This document discusses techniques for effective compression of digital video. It introduces several key algorithms used in video compression, including discrete cosine transform (DCT) for spatial redundancy reduction, motion estimation (ME) for temporal redundancy reduction, and embedded zerotree wavelet (EZW) transforms. DCT is used to compress individual video frames by removing spatial correlations within frames. Motion estimation compares blocks of pixels between frames to find and encode motion vectors rather than full pixel values, reducing file size. Combined, these techniques can achieve high compression ratios while maintaining high video quality for storage and transmission.
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
This document discusses a proposed method for lossless image compression that uses a hybrid of wavelet transforms, embedded zero tree coding, and Huffman coding. Specifically, it uses discrete wavelet transforms to decompose images into sub-bands, applies embedded zero tree coding to the wavelet coefficients to encode insignificant coefficients as zero trees, and then uses Huffman coding for further compression. Tables of results are presented showing compression ratios, bits per pixel, and PSNR values for images compressed with different threshold values in the wavelet decomposition. The goal is to determine an optimal threshold level for each decomposition to maximize compression while perfectly reconstructing the original image content.
This document summarizes a student project on implementing lossless discrete wavelet transform (DWT) and inverse discrete wavelet transform (IDWT). It provides an overview of the project, which includes introducing DWT, reviewing literature on lifting schemes for faster DWT computation, and simulating a 2D (5,3) DWT. The results show DWT blocks decomposing signals into high and low pass coefficients. Applications mentioned are in medical imaging, signal denoising, data compression and image processing. The conclusion discusses the need for lossless transforms in medical imaging. Future work could extend this to higher level transforms and applications like compression and watermarking.
This document discusses an enhanced technique for secure and reliable watermarking using Modified Haar Wavelet Transform (MFHWT). The proposed technique embeds a watermark into an original image using discrete wavelet transform (DWT) and wavelet packet transform (WPT) according to the size of the watermark. MFHWT is a memory efficient, fast, and simple transform. The watermarking process involves embedding and extraction processes. Various watermarking techniques in different transform domains are discussed, including DWT and WPT. The proposed algorithm uses MFHWT for decomposition and reconstruction. Image quality is measured using metrics like MSE and PSNR, with higher PSNR indicating better quality. The technique achieves robustness
Design and implementation of image compression using set partitioning in hier...eSAT Journals
Abstract
To store digital image and video in raw form require large amount of memory space. Image compression means reducing the size of image file without degrading quality of image. Depending on the reconstructed image to be exactly same as the original or some unknown loss may incurred image compression divided into two techniques lossy and lossless techniques. In this paper we present hybrid model which is the combination of several compression techniques. This paper present DWT, DCT and SPIHT implementation. Simulation has been carried out on different images like Lena , Barbra, Cameraman, Test drive . Result analysis is done through parameters like MSE, PSNR and Elapsed time. Values of this parameters will go better than the old algorithms because here we apply SPIHT lossless technique and also due to hybrid combination of DWT and DCT we will get good level of compression. The result analysis shows that the proposed hybrid algorithm performs much better in terms of PSNR with a higher compression ratio as compared to standalone DWT and DCT techniques.
Keywords- DWT, DCT, SPIHT, PSNR, MSE
Improvement in Traditional Set Partitioning in Hierarchical Trees (SPIHT) Alg...AM Publications
In this paper, an improved SPIHT algorithm based on Huffman coding and discrete wavelet transform for
image compression has been developed. The traditional SPIHT algorithm application is limited in terms of PSNR and
Compression Ratio. The improved SPIHT algorithm gives better performance as compared with traditional SPIHT
algorithm; here we used additional Huffman encoder along with SPIHT encoder. Huffman coding is an entropy
encoding algorithm uses a specific method for choosing the representation for each symbol, resulting in a prefix code.
The input gray scale image is decomposed by 'bior4.4' wavelet and wavelet coefficients are indexed and scanned by
DWT, then applied to encode the coefficient by SPIHT encoder followed by Huffman encoder which gives the
compressed image. At receiver side the decoding process is applied by Huffman decoder followed by SPIHT decoder.
The Reconstructed gray scale image looks similar to the applied gray scale image.
A novel technique in spiht for medical image compressionIAEME Publication
This document presents a novel technique for medical image compression using multi-wavelet transformation with modified fast Haar wavelet transform (MFHWT) in the Set Partitioning in Hierarchical Trees (SPIHT) algorithm. The technique aims to minimize information loss and increase image quality during compression. It involves applying multi-wavelet decomposition using MFHWT on the image, then using SPIHT to find significant and insignificant pixels. A region of interest is selected before compression to avoid loss of important image details. The technique is evaluated on medical images and shows improved peak signal-to-noise ratio and compression ratio compared to standard SPIHT.
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).
Modified Adaptive Lifting Structure Of CDF 9/7 Wavelet With Spiht For Lossy I...idescitation
We present a modified structure of 2-D cdf 9/7 wavelet
transforms based on adaptive lifting in image coding. Instead
of alternately applying horizontal and vertical lifting, as in
present practice, Adaptive lifting performs lifting-based
prediction in local windows in the direction of high pixel
correlation. Hence, it adapts far better to the image orientation
features in local windows. The predicting and updating signals
of Adaptive lifting can be derived even at the fractional pixel
precision level to achieve high resolution, while still
maintaining perfect reconstruction. To enhance the
performance of adaptive based modified structure of 2-D CDF
9/7 is coupled with SPIHT coding algorithm to improve the
drawbacks of wavelet transform. Experimental results show
that the proposed modified scheme based image coding
technique outperforms JPEG 2000 in both PSNR and visual
quality, with the improvement up to 6.0 dB than existing
structure on images with rich orientation features .
Analysis of image compression algorithms using wavelet transform with gui in ...eSAT Journals
Abstract Image compression is nothing but reducing the amount of data required to represent an image. To compress an image efficiently we use various techniques to decrease the space and to increase the efficiency of transfer of the images over network for better access. This paper explains about compression methods such as JPEG 2000, EZW, SPIHT (Set Partition in Hierarchical Trees) and HS-SPIHT on the basis of processing time, error comparison, mean square error, peak signal to noise ratio and compression ratio. Due to the large requirement for memory and the high complexity of computation, JPEG2000 cannot be used in many conditions especially in the memory constraint case. SPIHT gives better simplicity and better compression compared to the other techniques. But to scale the image more so as to get better compression we are using the line-based Wavelet transform because it requires lower memory without affecting the result of Wavelet transform. We proposed a highly scalable image compression scheme based on the Set Partitioning in Hierarchical Trees (SPIHT) algorithm. This algorithm is called Highly Scalable SPIHT (HS_SPIHT) it gives good scalability and provides 1 bit stream that can be easily adapted to give bandwidth and resolution requirements. Keywords: - Wavelet transform Scalability, SPIHT, HS-SPIHT, Processing time, Line-based Wavelet transform.
1) The document discusses using the SPIHT algorithm for compressing color images by first converting them from RGB to YCbCr color space and then applying wavelet transforms and SPIHT compression separately to the Y, Cb, and Cr components.
2) Simulation results using MATLAB show that SPIHT compression achieves high quality reconstruction for different bit rates and wavelet decomposition levels as measured by PSNR and MSE.
3) The SPIHT algorithm partitions wavelet coefficients into significant and insignificant sets to iteratively encode bit planes from most to least significant, allowing for embedded bitstream generation and rate control.
Efficient video compression using EZWTIJERA Editor
In this article, wavelet based lossy video compression algorithm is presented. The motion estimation and compensation, being an important part in the compression, is based on segment movements. The proposed work is based on wavelet transform algorithm Embedded Zeroed WaveletTransform (EZWT). Based on the results of peak signal to noise ratio (PSNR), mean squared error (MSE), different videos are analyzed. Maintaining the PSNR to acceptable limits the proposed EZWT algorithm achieves very good compression ratios making the technique more efficient than the 2-Discrete Cosine Transform (DCT) in the H.264/AVC codec. The method is being suitable for low bit rate video showing highest compression ratio and very good PSNR of more than 30dB.
IRJET- Wavelet Transform along with SPIHT Algorithm used for Image Compre...IRJET Journal
This document presents a method for medical image compression using lifting-based wavelet transform coupled with the SPIHT (Set Partition in Hierarchical Trees) algorithm. The lifting-based wavelet transform is used to decompose images into wavelet coefficients, addressing drawbacks of conventional wavelet transforms. SPIHT is then used to encode the wavelet coefficients. Experimental results showed the proposed method provides better PSNR, quality factor, and MSSIM values for medical images at low bit rates, compared to traditional compression methods. The compressed images can be represented in formats like GIF, JPG, BMP and PNG.
A Comprehensive lossless modified compression in medical application on DICOM...IOSR Journals
ABSTRACT : In current days, Digital Imaging and Communication in Medicine (DICOM) is widely used for
viewing medical images from different modalities, distribution and storage. Image processing can be processed
by photographic, optical and electronic means, because digital methods are precise, fast and flexible, image
processing using digital computers are the most common method. Image Processing can extract information,
modify pictures to improves and change their structure (image editing, composition and image compression
etc.). Image compression is the major entities of storage system and communication which is capable of
crippling disadvantages of data transmission and image storage and also capable of reducing the data
redundancy. Medical images are require to stored for future reference of the patients and their hospital findings
hence, the medical image need to undergo the process of compression before storing it. Medical images are
much important in the field of medicine, all these Medical image compression is necessary for huge database
storage in Medical Centre and medical data transfer for the purpose of diagnosis. Presently Discrete cosine
transforms (DCT), Run Length Encoding Lossless compression technique, Wavelet transforms (DWT), are the
most usefully and wider accepted approach for the purpose of compression. On basis of based on discrete
wavelet transform we present a new DICOM based lossless image compression method. In the proposed
method, each DICOM image stored in the data set is compressed on the basis of vertically, horizontally and
diagonally compression. We analyze the results from our study of all the DICOM images in the data set using
two quality measures namely PSNR and RMSE. The performance and comparison was made over each images
stored in the set of data set of DICOM images. This work is presenting the performance comparison between
input images (without compression) and after compression results for each images in the data set using DWT
method. Further the performance of DWT method with HAAR process is compared with 2D-DWT method using
the quality metrics of PSNR & RMSE. The performance of these methods for image compression has been
simulated using MATLAB.
Keywords: JPEG, DCT, DWT, SPIHT, DICOM, VQ, Lossless Compression, Wavelet Transform, image
Compression, PSNR, RMSE
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
Video Compression Algorithm Based on Frame Difference Approaches ijsc
The huge usage of digital multimedia via communications, wireless communications, Internet, Intranet and cellular mobile leads to incurable growth of data flow through these Media. The researchers go deep in developing efficient techniques in these fields such as compression of data, image and video. Recently, video compression techniques and their applications in many areas (educational, agriculture, medical …) cause this field to be one of the most interested fields. Wavelet transform is an efficient method that can be used to perform an efficient compression technique. This work deals with the developing of an efficient video compression approach based on frames difference approaches that concentrated on the calculation of frame near distance (difference between frames). The
selection of the meaningful frame depends on many factors such as compression performance, frame details, frame size and near distance between frames. Three different approaches are applied for removing the lowest frame difference. In this paper, many videos are tested to insure the efficiency of this technique, in addition a good performance results has been obtained.
Wavelet based Image Coding Schemes: A Recent Survey ijsc
A variety of new and powerful algorithms have been developed for image compression over the years. Among them the wavelet-based image compression schemes have gained much popularity due to their overlapping nature which reduces the blocking artifacts that are common phenomena in JPEG compression and multiresolution character which leads to superior energy compaction with high quality reconstructed images. This paper provides a detailed survey on some of the popular wavelet coding techniques such as the Embedded Zerotree Wavelet (EZW) coding, Set Partitioning in Hierarchical Tree (SPIHT) coding, the Set Partitioned Embedded Block (SPECK) Coder, and the Embedded Block Coding with Optimized Truncation (EBCOT) algorithm. Other wavelet-based coding techniques like the Wavelet Difference Reduction (WDR) and the Adaptive Scanned Wavelet Difference Reduction (ASWDR) algorithms, the Space Frequency Quantization (SFQ) algorithm, the Embedded Predictive Wavelet Image Coder (EPWIC), Compression with Reversible Embedded Wavelet (CREW), the Stack-Run (SR) coding and the recent Geometric Wavelet (GW) coding are also discussed. Based on the review, recommendations and discussions are presented for algorithm development and implementation.
International Journal on Soft Computing ( IJSC )ijsc
This document provides a summary of various wavelet-based image coding schemes. It discusses the basics of image compression including transformation, quantization, and encoding. It then reviews the wavelet transform approach and several popular wavelet coding techniques such as EZW, SPIHT, SPECK, EBCOT, WDR, ASWDR, SFQ, EPWIC, CREW, SR and GW coding. These techniques exploit the multi-resolution properties of wavelets for superior energy compaction and compression performance compared to DCT-based methods like JPEG. The document provides details on how each coding scheme works and compares their features.
HARDWARE SOFTWARE CO-SIMULATION OF MOTION ESTIMATION IN H.264 ENCODERcscpconf
This paper proposes about motion estimation in H.264/AVC encoder. Compared with standards
such as MPEG-2 and MPEG-4 Visual, H.264 can deliver better image quality at the same
compressed bit rate or at a lower bit rate. The increase in compression efficiency comes at the
expense of increase in complexity, which is a fact that must be overcome. An efficient Co-design
methodology is required, where the encoder software application is highly optimized and
structured in a very modular and efficient manner, so as to allow its most complex and time
consuming operations to be offloaded to dedicated hardware accelerators. The Motion
Estimation algorithm is the most computationally intensive part of the encoder which is simulated using MATLAB. The hardware/software co-simulation is done using system generator tool and implemented using Xilinx FPGA Spartan 3E for different scanning methods.
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.
The growing trend of online image sharing and downloads today mandate the need for better encoding and
decoding scheme. This paper looks into this issue of image coding. Multiple Description Coding is an
encoding and decoding scheme that is specially designed in providing more error resilience for data
transmission. The main issue of Multiple Description Coding is the lossy transmission channels. This work
attempts to address the issue of re-constructing high quality image with the use of just one descriptor
rather than the conventional descriptor. This work compare the use of Type I quantizer and Type II
quantizer. We propose and compare 4 coders by examining the quality of re-constructed images. The 4
coders are namely JPEG HH (Horizontal Pixel Interleaving with Huffman Coding) model, JPEG HA
(Horizontal Pixel Interleaving with Arithmetic Encoding) model, JPEG VH (Vertical Pixel Interleaving
with Huffman Encoding) model, and JPEG VA (Vertical Pixel Interleaving with Arithmetic Encoding)
model. The findings suggest that the use of horizontal and vertical pixel interleavings do not affect the
results much. Whereas the choice of quantizer greatly affect its performance.
Review On Fractal Image Compression TechniquesIRJET Journal
This document reviews different techniques for fractal image compression. It discusses how fractal image compression works by dividing an image into range and domain blocks. It then summarizes several papers that propose techniques for fractal image compression using discrete cosine transform (DCT) or discrete wavelet transform (DWT). These techniques aim to improve compression ratio and reduce encoding time. Finally, the document proposes a new method that combines wavelets with fractal image compression to further increase compression ratio while maintaining low image quality losses during decompression.
This document summarizes image compression techniques. It discusses how image compression aims to reduce the number of bits required to represent an image by removing spatial and spectral redundancies. The techniques are classified as lossless or lossy. Lossy techniques discussed include discrete cosine transform coding, discrete wavelet transform coding, vector quantization, fractal coding, and block truncation coding. Key metrics for evaluating compression performance and quality, such as mean square error and peak signal-to-noise ratio, are also defined.
This document summarizes various image compression techniques including lossy and lossless methods. For lossy compression, it discusses transformation coding techniques like discrete cosine transform (DCT) and discrete wavelet transform (DWT). It also covers vector quantization, fractal coding, block truncation coding, and subband coding. For lossless techniques, it describes run length encoding, Huffman coding, LZW coding, and area coding. Finally, it provides an overview of using neural networks for image compression, including backpropagation networks, hierarchical/adaptive networks, and multilayer perceptron networks.
This document summarizes image compression techniques. It discusses how image compression aims to reduce the number of bits required to represent an image by removing spatial and spectral redundancies. The techniques are classified as lossless or lossy. Lossy techniques discussed include discrete cosine transform coding, discrete wavelet transform coding, vector quantization, fractal coding, and block truncation coding. Key metrics for evaluating compression performance and quality, such as mean square error and peak signal-to-noise ratio, are also defined.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
This document summarizes a research paper that proposes a new data hiding technique for hiding data in compressed video files. The technique embeds data by modifying the least significant bits of motion vectors used during video compression. Motion vectors associated with higher prediction errors are selected as candidate motion vectors to embed data. An adaptive threshold is used for each frame to minimize prediction error while maximizing data payload. The data can be extracted directly from the encoded video stream without the original video. The technique was tested on standard video sequences and was found to introduce minimal distortion and overhead.
A REVIEW ON LATEST TECHNIQUES OF IMAGE COMPRESSIONNancy Ideker
This document reviews various techniques for image compression. It begins by discussing the need for image compression in applications like remote sensing, broadcasting, and long-distance communication. It then categorizes compression techniques as either lossless or lossy. Popular lossless techniques discussed include run length encoding, LZW coding, and Huffman coding. Lossy techniques reviewed are transform coding, block truncation coding, vector quantization, and subband coding. The document evaluates these techniques and compares their advantages and disadvantages. It also discusses performance metrics for image compression like PSNR, compression ratio, and mean square error. Finally, it reviews several research papers on topics like vector quantization-based compression and compression using wavelets and Huffman encoding.
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.
The Computation Complexity Reduction of 2-D Gaussian FilterIRJET Journal
This document discusses reducing the computational complexity of a 2D Gaussian filter for image smoothing. It begins with an abstract that notes 2D Gaussian filters are commonly used for image smoothing but require heavy computational resources. It then proposes using fixed-point arithmetic rather than floating point to implement the filter on an FPGA, which can increase efficiency and decrease area and complexity. The document is divided into sections that cover the theory behind image filtering, image smoothing and sharpening, quality metrics for evaluation, and an energy scalable Gaussian smoothing filter architecture. It concludes by discussing results and benefits of implementing the filter using fixed-point arithmetic on an FPGA.
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A High Performance Modified SPIHT for Scalable Image Compression
1. Bibhuprasad Mohanty, Abhishek Singh & Sudipta Mahapatra
International Journal of Image processing (IJIP), Volume (5) : Issue (4) : 2011 390
A High Performance Modified SPIHT for
Scalable Image Compression
Bibhuprasad Mohanty bmohanty.iit07@gmail.com.
Assistant professor,
Institute of Technical Education and Research
SoA University, Bhubaneswar
Abhishek Singh abhishek.uor@gmail.com.
Assistant professor
Faculty of Science and Technology,
ICFAI University, Tripura
Dr. Sudipta Mahapatra sudipta.mahapatra@gmail.com
Associate Professor,
Department of E &ECE,
Indian Institute of Technology, Kharagpur, India
Abstract
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.
Key Words : Zero Shifting, 2D SPIHT, Lifting Wavelet Transform, Context Modeling, Resolution
Scalability, SSIM
1. INTRODUCTION
Image compression algorithms manipulate images to dramatically reduce the storage and
bandwidth required while maximizing perceived image quality. Image compression based on
discrete wavelet transform (DWT) has emerged as one of the popular tool. The wavelet domain
provides a natural setting for many applications which involve real world signals. The presence
of blocking artifacts at low bit rate in the entropy coding schemes (e.g., Discrete Cosine
Transform) is completely avoided in this wavelet based coding. This transform achieves better
energy compaction than the DCT and hence can help in providing better compression for the
same quality measurement, like peak-signal-to-noise-ratio (PSNR) [1] and structural similarity
(SSIM) [2].
2. Bibhuprasad Mohanty, Abhishek Singh & Sudipta Mahapatra
International Journal of Image processing (IJIP), Volume (5) : Issue (4) : 2011 391
The widespread deployment of diversified and heterogeneous digital networks requires different
bandwidth range for various user nodes. This heterogeneity property demands for scalability to
optimally serve each user according to the available bandwidth and computing capabilities. The
goal of the scalable compression is to enable clients to reconstruct the image from a single
compressed bit stream, according to their specific capabilities. A scalable coder generates a bit
stream which consists of a set of embedded parts that offer better SNR and/or greater spatial
resolution. Different types of decoders with different complexity and various access bandwidths
can be accommodated in the case of an entirely scalable bit stream,
Based on DWT, several competitive image compression coders with high coding efficiency have
been developed. Among those coders, Shapiro’s Embedded Zero tree Wavelet (EZW)
compression [3], Said and Pearlman’s set portioning in Hierarchical Trees (SPIHT) [4], and
Tubman’s Embedded Block Coding with Optimized Truncation (EBCOT) [5] are the benchmark
contribution. The 2-D SPIHT coder performs competitively with most of the other coders
published in the literature [6]. The encoded and compressed SPIHT bit stream is further
compressed by applying adaptive arithmetic encoder which provides an additional PSNR gain of
0.2 to 0.6 dB [4] over the original one. The low margin PSNR improvement shows the fact that
SPIHT itself is exploiting the redundancies across the sub band so efficiently that it leaves little
room for further compression by the arithmetic coder. However, use of arithmetic coder increases
the computational complexity and hence the scheme sacrifices its speed for a meager gain.
Further improvements of SPIHT have been published in [7-12]. In [13], the authors proposed a
novel modification in exploiting the correlation of coefficients by lifting based DWT with 1D
addressing method instead of traditional 2D method. Also, for the purpose of hardware
implementation, they modified the SPIHT algorithm by interchanging the refinement pass and the
sorting pass. Although almost all of the state-of-the-art zero tree-based image compression
methods are SNR scalable and provide bit streams for progressive (by quality) image
compression, they do not explicitly support spatial scalability and do not provide a bitstream
which can be adapted easily to a heterogeneous environment. PROGRES [12] brings the concept
of resolution scalability as well as faster encoding and decoding into the SPIHT, but it loses
desirable feature like embeddedness and SNR scalable decoding.
In this paper, we present a new scheme which provides significant improvement in the quality of
the reconstructed image in terms of PSNR and SSIM without using the adaptive arithmetic
encoder. The method, called zero shifting, is applied on the spatial domain of the image before it
is wavelet transformed [18]. To accommodate the zero shifting schemes without sacrificing the
inherent property of the SPIHT codec, we have modified the original algorithm in [4]. Further, we
modeled the transformed coefficient according to its significance in a 2x2 adjacent offspring group
as a fixed context. The systematic trend in probability distribution of such a context has been
utilized for further compression of the image with the help of Huffman coding which is of lower
complexity than the arithmetic coding [19]. We also propose an approach to resolution scalability
by grouping together the bit streams generated for each resolution at one location. Both the
sorting and refinement pass for SPIHT encoder is performed for each resolution and certain
header information are generated which indicate the boundary condition for that resolution. The
paper is organized as follows: Section 2 reviews the Lifting based wavelet transform and SPIHT
coder briefly. The motivations for the proposed schemes are discussed in Section 3. The codec
structure is described in Section 4 and we have discussed the results elaborately in Section 5.
The conclusions of this paper are reported in Section 6.
2. BRIEF REVIEW
2.1Lifting Based Wavelet Transform (WT)
Wavelet transforms provide both spatial and frequency-domain information about an image or a
sequence of frames [14]. Such multiresolution representations of images/frames are often a more
convenient format for image coding. This is particularly important since Human Vision System
3. Bibhuprasad Mohanty, Abhishek Singh & Sudipta Mahapatra
International Journal of Image processing (IJIP), Volume (5) : Issue (4) : 2011 392
(HVS) is less sensitive to high-frequency distortions than low-frequency ones. WT analyzes a
signal at different frequency bands with different resolutions by decomposing it into a
coarse approximation and detail information. The classical 2D transform is performed by two
separate 1D transforms along the rows and the columns of the image data, resulting at each
decomposition step in a low pass image or the coarse scale approximation, and three detail
images. Because of their inherent multiresolution signal representation, wavelet-based coding
schemes have the potential to support both SNR and spatial scalability [15].
The lifting scheme is a fast and efficient method to realize biorthogonal wavelet transforms [16]. It
is simple, fast and reversible in computation due to the use of integer transformation. The
algorithm has the advantages of using lifting, instead of convolution, which reduces the memory
requirement. This influences the lossless compression algorithm profoundly because of its much
wider dynamic range and hence JPEG 2000 uses the scheme. Even 3D wavelet decomposition is
computed by applying three separate 1D transforms along the video data: rows and columns of
the image as two directions and time as the third direction.
2.2Set Partitioning In Hierarchical Trees (SPIHT)
SPIHT is an embedded coding technique. In embedded coding algorithms, encoding of the same
signal at lower bit rate is embedded at the beginning of the bit stream for the target bit rate.
Effectively, bits are ordered in importance. This type of coding is especially useful for progressive
transmission using an embedded code; where an encoder can terminate the encoding process at
any point. SPIHT algorithm is based on following concepts [4]:
1. Ordered bit plane progressive transmission.
2. Set partitioning sorting algorithm.
3. Spatial orientation trees.
SPIHT keeps three lists: LIP, LSP, and LIS. LIP stores insignificant pixels, LSP stores
significant pixels and LIS stores insignificant sets. At the beginning, LSP is empty, LIP keeps all
coefficients in the lowest sub band, and LIS keeps all tree roots which are at the lowest sub
band. SPIHT starts coding by running two passes. The first pass is the sorting pass. It first
browses the LIP and moves all significant coefficients to LSP and outputs its sign. Then it
browses LIS executing the significance information and following the partitioning sorting
algorithms.
The second pass is the refining pass. It browses the coefficients in LSP and outputs a single
bit alone based on the current threshold. After the two passes are finished, the threshold is
divided by 2 and the encoder executes the two passes again. This procedure is recursively
applied until the number of output bits reaches the desired number.
3. THE PROPOSED SCHEME
3.1The Zero Shifting
The method of zero shifting is a simple and easy-to-implement technique which preserves the
embedded property of the SPIHT coding. By downward scaling of the pixel values by 2
(N-1)
, N
being the number of bits representing the original pixel, the spatial domain magnitude becomes
bipolar ranging from (-2
(N-1)
) to (+2
(N-1)
-1), with almost half of the maximum absolute spatial value.
In the transform domain, the wavelet lifting scheme produces the high pass sub band by taking
the weighted differences of the pixel values after prediction. Similarly, the low pass sub band is
produced by taking the weighted average of the pixel values after the updating step. This low
pass sub band is further decomposed iteratively for each level of decomposition to finally provide,
one lowest frequency band, the rest being high frequency bands.
SPIHT coding is a method of bit plane coding technique. The highest number bit plane is decided
by the maximum absolute value of the transformed coefficients. As the maximum coefficient
decreases by the method of zero shifting, SPIHT algorithm takes less time for encoding as well
as for decoding. Further, the lower absolute image values in the spatial domain (which originally
ranges from 0≤pi,j≤ 2N-1
) are shifted to higher absolute range in the bipolar sense. The lower
4. Bibhuprasad Mohanty, Abhishek Singh & Sudipta Mahapatra
International Journal of Image processing (IJIP), Volume (5) : Issue (4) : 2011 393
image values are detailed information pertaining to the boundaries, edges, etc. These coefficients
become significant at a higher threshold value and accordingly are ordered earlier in the bit plane
progressive transmission of the SPIHT bit stream. Since SPIHT algorithm always encodes a sign
bit for significant coefficients, no extra bit budget is necessary for being bipolar. By terminating
the decoding process even at a lower bit rate, the quality improves, both subjectively and
objectively, because of the inclusion of some detail information at that rate.
3.2 Modified SPIHT
The parent offspring dependency and corresponding spatial orientation trees for the SPIHT
algorithm is as shown in Fig. 1 for three levels of decomposition. Each node of the tree is
represented by its coordinates in the algorithm. The tree is defined in such a way that each node
has no offspring (leaves) or four off springs at the same spatial location in the next finer level [4].
In original SPIHT, the pixels in the coarsest level e.g., LL sub band of the 3rd level for Fig. 1, are
the tree roots and they are grouped into blocks of 2x2 adjacent pixels. In each block one (the left
top corner) pixel has no descendant. In the initialization stage, the coordinates of all coefficients
in the coarsest level are put into LIP list and the coordinates of coefficients having descendants
only into the LIS list as D-type sets. We have modified the initialization stage of the SPIHT
algorithm to accommodate the principle of zero shifting as follows: if the decomposition level is
such that the coarsest level LL subband consists of a single pixel (e.g., for an image of 512x512
size decomposed into 9 level), then we set the LIS list with the HL, LH, and HH coordinates of the
coarsest level (each of which is also a single pixel). Otherwise, we initialize the LIS list with all the
coordinates of the LL sub band as of the LIP list. Accordingly, in the sorting pass for LIS, the
SPIHT first tests the significance in the LH, HL, and HH band of the lowest subband. For each
root in LL, it compares the maximum value between the other three bands at the same spatial
orientation and sorts it in the output bit stream. Once this test is over, the set partition rule of
SPIHT [6] divides the remaining descendants into either D-type set or L-type set. The decoder
duplicates the path of execution of the encoder. At the encoder we are at a liberty to encode the
entire bit plane without any bit budget constraint. Experiments carried out over different type of
images with spatially different features show that the proposed modification provides a faster
codec than the original one while resulting almost the same PSNR values.
3.3: Fixed Context Modeling
SPIHT algorithm generates significant information during encoding of LIS and LIP. LIP is not
supposed to generate any redundancies as they are distinctly decorrelated. When LIS of type A
becomes significant at certain threshold, the significance information of offspring of 2x2 blocks
are generated. We exploited the possibility of such four offspring being significant. Assigning 0 to
insignificant and 1 to significant coefficient, we created 16 possible combination of such
configuration, starting from 0000 to 1111. We called each configuration a context and found the
probability of occurrence of each context model (Fig. 6A and 6B). The systematic trend found in
FIGURE 1. Spatial Orientation tree in 2D SPIHT (source [8])
5. Bibhuprasad Mohanty, Abhishek Singh & Sudipta Mahapatra
International Journal of Image processing (IJIP), Volume (5) : Issue (4) : 2011 394
probability distribution of such context has been utilized for further compression of the image with
the help of Huffman coding which has a lower complexity than the arithmetic coding.
3.4 Fixed Context Resolution Scalability (FCRS)
SPIHT algorithm provides SNR (quality) scalability due to its bit plane coding strategy but fails to
provide resolution scalability. Each bit plane is coded in two passes – Significant pass and then
Refinement pass. Each pass generates the bitstream as a whole in an interleaved fashion without
distinguishing between any resolutions so needed (Fig 2(a)). If the decoder wants to decode an
image at a given resolution then all the information carrying bits of that resolution should be
available at one place. In addition, the knowledge of boundary of separation of different resolution
should be available. We have modified the bitstream structure in a way so as to gather all the
information pertaining to a particular resolution at a place as shown in Fig.2 (b), where we have
shown only three resolutions as per our simulation. It is to be noted that we have incorporated the
context modeling into the SPIHT algorithm. Here, the bitstream for both the passes are adjacent
to each other corresponding to different individual resolution. Thus, coding of each bit plane
should be split in parts depending upon the specific resolution required and each part should
contain the corresponding information generated by both the significance and refinement passes
for that resolution.
FIGURE 2(a). Structure of output bit stream FIGURE 2(b). Modified Structure
For each resolution, the sorting pass is carried out following the refinement pass for all the
threshold values and kept in the output bitstream as per the progressive bit plane coding. Starting
from the resolution 1, the algorithm encodes the entire image and generates a header which
carries the information regarding the boundary for a particular resolution. Say, for resolution 1,
we process those LIS of type A whose offspring are also lying in the resolution level 1 only.
Similarly, while processing LIP, we are processing only those coefficients which belong to the
same resolution. The header, which stores all the boundary information, is sent to the decoder.
The reconstruction at the decoder at a particular resolution level is done using the header
information.
4. THE CODEC STRUCTURE
The well known SPIHT algorithm encodes the wavelet transformed coefficient in a bit plane
manner and outputs the significant information with respect to a specified threshold and its sign.
We pre processed the pixel values by shifting downwards by 2
N-1
, N being the no of bits in the
original image, and then applied the 2D wavelet on it. The transformed coefficient is then
encoded with 2D SPIHT coder (Fig.3).
Significant information,
s1, s2, s3
Refinement information,
r1, r2, r3
s1 r1 s2 r2 s3 r3
6. Bibhuprasad Mohanty, Abhishek Singh & Sudipta Mahapatra
International Journal of Image processing (IJIP), Volume (5) : Issue (4) : 2011 395
FIGURE 3. Proposed image codec with zero shifting
In the decoder, the execution path of the encoder is duplicated and the values of inverse
transformed coefficients are shifted upwards by 2
(N-1)
to get back the original image.
5. RESULTS AND DISCUSSION
5.1Quality Measurement Metrics
During the process of said compression, the reconstructed image is subject to a wide variety of
distortion. Subjective evaluations emphasizes on the visual image quality, which is too
inconvenient, time consuming, and complex. The objective image quality metrics like Peak Signal
to Noise Ratio (PSNR), or Mean Squared Error (MSE) and structural Similarity (SSIM) [2] are
thought to be the best for the image processing application.
The MSE metric is most widely used for it is simple to calculate, having clear physical
interpretation and mathematically convenient. MSE is computed by averaging the squared
intensity difference of reconstructed image, and the original image, x. Then from it the PSNR is
calculated. Mathematically,
, (1)
Where, MxN is the size of the image and assuming the grey scale image of 8 bits per pixel (bpp),
the PSNR is defined as,
(2)
But, they are not very well matched to human visual perception. The authors in [2] have shown
that even for the same PSNR values, the visual quality for two reconstructed images is different
at different bit rate. It is because the pixels of the natural images are highly structured and exhibit
strong dependencies. The SSIM system separates the task of similarity measurement into three
comparisons: luminance, contrast, and structure. The overall similarity measure is defined as
[2,17]
(3)
Where, x is the original assuming to be completely perfect and is the reconstructed image such
that both are non negative images. S=1 implies a perfect similarity between the images.
l(x, ) = luminance comparison function, which in turn the function of the mean values
of x and ;
c(x, ) = contrast comparison function and they are function of the standard deviation of
x and ; and
7. Bibhuprasad Mohanty, Abhishek Singh & Sudipta Mahapatra
International Journal of Image processing (IJIP), Volume (5) : Issue (4) : 2011 396
s(x, ) = structure comparison function
5.2 Algorithm Complexity in Terms of Encoding/decoding Time
We will present some evidence of our implementation with Matlab in this section. Experiments are
performed with grey scale ‘Lena’ and ‘Ship’ image for their established popularity. Ship image
contains more details than Lena image. All experiments are done with Pentium IV, 1.76 GHz, 512
MB RAM. We have considered two type of filters for their establish popularity in the image
compression application. They are ‘bior4.4’ and ‘cdf9/7’ with half point symmetry at the boundary.
It is evident from the Table 1 that, with modification of the algorithm, the time of encoding
decreases significantly, i.e., the codec becomes faster irrespective of the type of the filter used.
The modified algorithm takes 10 to 90 times less time than that for the original one. Interestingly,
the encoding time goes on decreasing as the number of decomposition increases from 3 to 9 (for
square image of resolution size 512x512) in steps of 1, whereas the time increases almost
linearly with the original one. By the method of zero shifting, the original algorithm takes even
more time for encoding than that for without the said pre processing steps. It is found that for the
modified algorithm, the encoding time remains almost constant for 6th level of decomposition or
more. So for other experiments with the modified algorithm, we have spatially decomposed the
image for 8 levels, unless otherwise stated. After verifying the complexity in terms encoding time,
we are conducting the following experiment to verify the quality of the reconstructed image with
the proposed scheme of zero shifting. The following Table 2 and Table 3 provide some detail of
decoding time, the PSNR value and SSIM for the reconstructed LENA image at different bit rate
for both the algorithms with ‘bior4.4 filter.
Level of
decomposition
With
BM
With CM With
BMZ
With
CMZ
With BO With CO With
BOZ
With
COZ
3 3.4735 3.2595 3.1445 2.9640 35.6672 34.6788 40.9529 41.2126
4 2.1118 2.0357 1.8985 1.9531 68.7433 739693 75.9793 86.8388
5 1.6166 1.5989 1.5829 1.6727 87.8300 88.0677 102.1387 88.9418
6 1.4674 1.5062 1.4425 1.4883 90.3934 92.3333 89.8692 96.6292
7 1.4110 1.5085 1.4085 1.4189 87.7200 93.4404 94.7028 93.2454
8 1.3989 1.5181 1.3745 1.4009 95.7797 93.2782 97.9520 94.9666
9 1.8483 1.4252 1.4139 1.4035 98.4183 104.0956 95.5076 110.1410
B (C) M (O) : With Bior4.4 (CDF 9/7) filter for Modified ( Original) SPIHT algorithm with 1bpp,
Z : with method of Zero-shifting
TABLE 1: Complexity Comparison in terms of Encoding Time (sec)
8. Bibhuprasad Mohanty, Abhishek Singh & Sudipta Mahapatra
International Journal of Image processing (IJIP), Volume (5) : Issue (4) : 2011 397
TABLE 2 : Decoding time, PSNR and SSIM for reconstructed LENA with Modified SPIHT
It is to be noted that the modified algorithm encodes the image for all the bitplane; whereas the
original is restricted to the specified bit budget. However, it is possible to encode with the
modified algorithm with the same bit budget constraints. It is evident from the observations from
both these tables that the modification in the algorithm makes the decoding faster. The objective
metric like PSNR also improves due to modification in the initialization of the image pixel
coordinate and hence in the scanning process. However, the SSIM metric is better for the original
one at some bit rate. In the following we are showing the comparison curves for LENA and SHIP
images. The experiments are performed with 6 level of spatial decomposition with bior4.4 filter.
5.3 Results with Proposed Image Codec
We have found that 6 or more level of spatial decomposition provides almost constant PSNR for
any bit rate. Fig. 4 and 5 shows the performances of PSNR of the Lena and Ship image at
different bpp. It is quite evident from the curves that the modified algorithm significantly improves
the performances for all bit rates. The improvement is more pronounced in Lena than in Ship,
because Ship contains more detail information which are not properly exploited by the SPIHT
algorithm. At the lowest possible decoding rate (i.e., 0.1 to 0.2), where the perceptual quality is
significantly good, our zero shifting scheme provides almost 0.5 to 1dB higher PSNR than the
original one. Experiments show that at 0.15 bpp the visual quality is quite good. Further, on
wavelet transforming the shifted pixel values, coefficients are generated which are mostly
decorrelated in sign. Experimental observations reveal that these correlations are more
pronounced in the lowest frequency sub band.
Bit
rate
in
(bpp)
Decodin
g time
(sec)
without
Zero
shifting
Decoding
time (sec)
with Zero
shifting
SSIM
without
Zero
shifting
SSIM
with
Zero
shifting
PSNR
without
Zero
shifting
PSNR
with
Zero
shifting
0.01 0.06 0.03 0.4574 0.5298 17.77 20.89
0.11 0.14 0.05 0.8425 0.8464 26.98 28.67
0.21 0.22 0.07 0.9077 0.9125 29.95 31.59
0.31 0.26 0.10 0.9340 0.9435 32.46 32.87
0.41 0.33 0.13 0.9550 0.9532 33.69 34.78
0.51 0.35 0.13 0.9638 0.9671 35.31 35.51
0.61 0.44 0.16 0.9674 0.9716 35.85 36.07
0.71 0.53 0.20 0.9705 0.9744 36.35 36.53
0.81 0.60 0.25 0.9790 0.9784 37.31 37.98
0.91 0.62 0.24 0.9818 0.9826 38.28 38.44
9. Bibhuprasad Mohanty, Abhishek Singh & Sudipta Mahapatra
International Journal of Image processing (IJIP), Volume (5) : Issue (4) : 2011 398
TABLE 3: Decoding time, PSNR and SSIM for reconstructed LENA with Original SPIHT
Since in the process of SPIHT encoding we are at a liberty to invest an extra bit always for testing
any significance, the reconstruction at the lower bit rate is better. Also, due to efficient grouping of
the insignificant information in LIS with our proposed scheme, the no of bits to be transmitted to
decoder also reduced to a factor of 4. Hence truncating the bit stream at lower bit rate provides
much of the significant information to the decoder and the reconstruction quality also improves.
FIGURE 4.Comparision of Lena image FIGURE 5.Comparision of Ship image
Bit
rate
in
(bpp)
Decoding
time
(sec)
without
Zero
shifting
Decoding
time
(sec)
with
Zero
shifting
SSIM
without
Zero
shifting
SSIM
with
Zero
shifting
PSNR
without
Zero
shifting
PSNR
with
Zero
shifting
0.01 0.039 0.043 0.4697 0.4093 16.72 20.59
0.11 1.005 0.953 0.8590 0.8622 28.30 29.4.
0.21 2.501 2.64 0.9272 0.9221 31.61 32.38
0.31 4.483 5.739 0.9536 0.9517 33.69 34.11
0.41 9.178 9.002 0.9639 0.9629 35.03 35.60
0.51 12.995 17.53 0.9729 0.9728 36.37 36.62
0.61 21.854 24.955 0.9776 0.9765 37.12 37.39
0.71 33.991 38.809 0.9804 0.9795 37.83 38.05
0.81 47.440 49.256 0.9830 0.9828 38.46 38.74
0.91 58.591 65.62 0.9855 0.9857 39.18 39.37
10. Bibhuprasad Mohanty, Abhishek Singh & Sudipta Mahapatra
International Journal of Image processing (IJIP), Volume (5) : Issue (4) : 2011 399
5.4 Result With Context Modeling and Scalability
Extensive experiments are carried out with lot many images to find the pattern of the probability
distribution of the significant coefficient in the 2x2 blocks. We found that the distribution of almost
all the images of varying characteristics show a fixed pattern. The distribution for two
characteristically different grey scale images are shown; Lena image with much less detail within
the sub band (Fig. 6A), and Ship image with much high details (Fig. 6B). By taking the average of
this probability distribution we model that context.
FIGURE 6A. Context Count for Lena
We prepared a table to store the generated Huffman codes corresponding to each model and at
the time of encoding, we utilized this table directly. We compared the result of this context
modeling with that of the arithmetic encoding. We define 128x128 sizes as resolution level 1,
256x256 as resolution level 2 and 512x512 as resolution level 3. All results are obtained by
applying 6 level of spatial wavelet decomposition with ‘bior 4.4’ filter. The modified SPIHT
encoder with the method of zero shifting is set to produce a bit stream that supports three
number of spatial scalability levels as mentioned above. The PSNR calculation is carried out with
the following formula:
(4)
Where, MSE is the Mean Squared Error calculated between reconstructed image at a particular
resolution and the original image of the same size as in eqn (2).
11. Bibhuprasad Mohanty, Abhishek Singh & Sudipta Mahapatra
International Journal of Image processing (IJIP), Volume (5) : Issue (4) : 2011 400
FIGURE 6B. Context Count for Ship
The rate distortion curves for each resolution in Fig.7 and the corresponding reconstructed LENA
images in Fig.8 clearly shows that our proposed encoder completely keeps the progressiveness
(by SNR) property of the original SPIHT algorithm. For resolution level 1 and 2, our resolution
scalable decoder obtained the proper bit stream tailored by the encoder for that resolution level.
FIGURE 7. Performance comparison of FCRS SPIHT for different resolution
12. Bibhuprasad Mohanty, Abhishek Singh & Sudipta Mahapatra
International Journal of Image processing (IJIP), Volume (5) : Issue (4) : 2011 401
(a) Resolution 1 (128x128) (b) Resolution 2 (256x256)
(c) Resolution 3(512x512)
FIGURE 8. FCRS reconstructed Lena at 0.5 bpp for 3 different level
As expected, the performance of scalable decoder is much better than for SPIHT and as the
resolution level becomes smaller (hence the scale becomes larger), the difference becomes more
significant. All the results are obtained with the context modeling of the encoded bit stream.
6. CONCLUSION
In this paper, we presented a novel extension of wavelet based SPIHT coding scheme. The
scheme pre processed the pixel values around zero before any wavelet transform takes place.
Higher PSNR and more compression are obtained with the scheme without using the popular
arithmetic coding. Experimental results show that it performs better measurably and also at lower
bit rate its performance is well suited for the purpose of surveillance and monitoring system. We
have presented an extension to the original SPIHT by incorporating spatial scalability feature to
the encoded bit stream. The bit stream can be stopped at any point to meet a bit budget during
the process of coding and decoding for a particular resolution level. To lessen the burden of
computation on the codec, we proposed a Huffman coding based context modeling to achieve
further compression at each resolution level. This technique can be extended for combined SNR,
spatial and frame rate scalable video coding.
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