This paper presents a new technique able to provide a very good compression ratio in preserving the quality of the important components of the image called main objects. It focuses on applications where the image is of large size and consists of an object or a set of objects on background such as identity photos. In these applications, the background of the objects is in general uniform and represents insignificant information for the application. The results of this new techniques show that is able to achieve an average compression ratio of 29% without any degradation of the quality of objects detected in the images. These results are better than the results obtained by the lossless techniques such as JPEG and TIF techniques.
Project Report on Medical Image Compression submitted for the award of B.Tech degree in Electrical and Electronics Engineering by Paras Prateek Bhatnagar, Paramjeet Singh Jamwal, Preeti Kumari and Nisha Rajani during session 2010-11.
An Algorithm for Improving the Quality of Compacted JPEG Image by Minimizes t...ijcga
The Block Transform Coded, JPEG- a lossy image compression format has been used to keep storage and
bandwidth requirements of digital image at practical levels. However, JPEG compression schemes may
exhibit unwanted image artifacts to appear - such as the ‘blocky’ artifact found in smooth/monotone areas
of an image, caused by the coarse quantization of DCT coefficients. A number of image filtering
approaches have been analyzed in literature incorporating value-averaging filters in order to smooth out
the discontinuities that appear across DCT block boundaries. Although some of these approaches are able
to decrease the severity of these unwanted artifacts to some extent, other approaches have certain
limitations that cause excessive blurring to high-contrast edges in the image. The image deblocking
algorithm presented in this paper aims to filter the blocked boundaries. This is accomplished by employing
smoothening, detection of blocked edges and then filtering the difference between the pixels containing the
blocked edge. The deblocking algorithm presented has been successful in reducing blocky artifacts in an
image and therefore increases the subjective as well as objective quality of the reconstructed image.
Matlab Based Image Compression Using Various Algorithmijtsrd
Image Compression is extremely intriguing as it manages this present reality issues. It assumes critical part in the exchange of information, similar to a picture, from one client to other. This paper exhibits the utilization MATLAB programming to execute a code which will take a picture from the client and returns the compacted structure as a yield. WCOMPRESS capacity is utilized which incorporates wavelet change and entropy coding ideas. This paper displays the work done on different sorts of pictures including JPEG (Joint Photographic Expert Group), PNG and so on and broke down their yield. Different pressure procedures like EZW, WDR, ASWDR, and SPIHIT which are exceptionally regular in picture handling are utilized Beenish Khan | Ms. Poonam | Mr. Mohammad Talib"Matlab Based Image Compression Using Various Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd14394.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/14394/matlab-based-image-compression-using-various-algorithm/beenish-khan
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.
This document compares the performance of three lossless image compression techniques: Run Length Encoding (RLE), Delta encoding, and Huffman encoding. It tests these algorithms on binary, grayscale, and RGB images to evaluate compression ratio, storage savings percentage, and compression time. The results found that Delta encoding achieved the highest compression ratio and storage savings, while Huffman encoding had the fastest compression time. In general, the document evaluates and compares the performance of different lossless image compression algorithms.
This document is a mini project report on digital image processing using MATLAB. It discusses various image processing techniques and applications implemented in MATLAB, including image formats, operations, and tools. Applications demonstrated include text recognition, color tracking, solving an engineering problem using image processing, creating a virtual slate using laser tracking, face detection, and distance estimation. The report provides examples of MATLAB functions used for tasks like importing, displaying, converting and cropping images, as well as analyzing and manipulating them.
International Journal of Computational Engineering Research(IJCER) ijceronline
nternational Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Improving image resolution through the cra algorithm involved recycling proce...csandit
Image processing concepts are widely used in medical fields. Digital images are prone to a
variety of types of noise. Noise is the result of errors in the image acquisition process for
reconstruction that result in pixel values that reflect the true intensities of the real scenes. A lot
of researchers are working on the field analysis and processing of multi-dimensional images.
Work previously hasn’t sufficient to stop them, so they continue performance work is due by the
researcher. In this paper we contribute a novel research work for analysis and performance
improvement about to image resolution. We proposed Concede Reconstruction Algorithm (CRA)
Involved Recycling Process to reduce the remained problem in improvement part of an image
processing. The CRA algorithms have better response from researcher to use them
Project Report on Medical Image Compression submitted for the award of B.Tech degree in Electrical and Electronics Engineering by Paras Prateek Bhatnagar, Paramjeet Singh Jamwal, Preeti Kumari and Nisha Rajani during session 2010-11.
An Algorithm for Improving the Quality of Compacted JPEG Image by Minimizes t...ijcga
The Block Transform Coded, JPEG- a lossy image compression format has been used to keep storage and
bandwidth requirements of digital image at practical levels. However, JPEG compression schemes may
exhibit unwanted image artifacts to appear - such as the ‘blocky’ artifact found in smooth/monotone areas
of an image, caused by the coarse quantization of DCT coefficients. A number of image filtering
approaches have been analyzed in literature incorporating value-averaging filters in order to smooth out
the discontinuities that appear across DCT block boundaries. Although some of these approaches are able
to decrease the severity of these unwanted artifacts to some extent, other approaches have certain
limitations that cause excessive blurring to high-contrast edges in the image. The image deblocking
algorithm presented in this paper aims to filter the blocked boundaries. This is accomplished by employing
smoothening, detection of blocked edges and then filtering the difference between the pixels containing the
blocked edge. The deblocking algorithm presented has been successful in reducing blocky artifacts in an
image and therefore increases the subjective as well as objective quality of the reconstructed image.
Matlab Based Image Compression Using Various Algorithmijtsrd
Image Compression is extremely intriguing as it manages this present reality issues. It assumes critical part in the exchange of information, similar to a picture, from one client to other. This paper exhibits the utilization MATLAB programming to execute a code which will take a picture from the client and returns the compacted structure as a yield. WCOMPRESS capacity is utilized which incorporates wavelet change and entropy coding ideas. This paper displays the work done on different sorts of pictures including JPEG (Joint Photographic Expert Group), PNG and so on and broke down their yield. Different pressure procedures like EZW, WDR, ASWDR, and SPIHIT which are exceptionally regular in picture handling are utilized Beenish Khan | Ms. Poonam | Mr. Mohammad Talib"Matlab Based Image Compression Using Various Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd14394.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/14394/matlab-based-image-compression-using-various-algorithm/beenish-khan
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.
This document compares the performance of three lossless image compression techniques: Run Length Encoding (RLE), Delta encoding, and Huffman encoding. It tests these algorithms on binary, grayscale, and RGB images to evaluate compression ratio, storage savings percentage, and compression time. The results found that Delta encoding achieved the highest compression ratio and storage savings, while Huffman encoding had the fastest compression time. In general, the document evaluates and compares the performance of different lossless image compression algorithms.
This document is a mini project report on digital image processing using MATLAB. It discusses various image processing techniques and applications implemented in MATLAB, including image formats, operations, and tools. Applications demonstrated include text recognition, color tracking, solving an engineering problem using image processing, creating a virtual slate using laser tracking, face detection, and distance estimation. The report provides examples of MATLAB functions used for tasks like importing, displaying, converting and cropping images, as well as analyzing and manipulating them.
International Journal of Computational Engineering Research(IJCER) ijceronline
nternational Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Improving image resolution through the cra algorithm involved recycling proce...csandit
Image processing concepts are widely used in medical fields. Digital images are prone to a
variety of types of noise. Noise is the result of errors in the image acquisition process for
reconstruction that result in pixel values that reflect the true intensities of the real scenes. A lot
of researchers are working on the field analysis and processing of multi-dimensional images.
Work previously hasn’t sufficient to stop them, so they continue performance work is due by the
researcher. In this paper we contribute a novel research work for analysis and performance
improvement about to image resolution. We proposed Concede Reconstruction Algorithm (CRA)
Involved Recycling Process to reduce the remained problem in improvement part of an image
processing. The CRA algorithms have better response from researcher to use them
Color image compression based on spatial and magnitude signal decomposition IJECEIAES
In this paper, a simple color image compression system has been proposed using image signal decomposition. Where, the RGB image color band is converted to the less correlated YUV color model and the pixel value (magnitude) in each band is decomposed into 2-values; most and least significant. According to the importance of the most significant value (MSV) that influenced by any simply modification happened, an adaptive lossless image compression system is proposed using bit plane (BP) slicing, delta pulse code modulation (Delta PCM), adaptive quadtree (QT) partitioning followed by an adaptive shift encoder. On the other hand, a lossy compression system is introduced to handle the least significant value (LSV), it is based on an adaptive, error bounded coding system, and it uses the DCT compression scheme. The performance of the developed compression system was analyzed and compared with those attained from the universal standard JPEG, and the results of applying the proposed system indicated its performance is comparable or better than that of the JPEG standards.
This document provides an exploratory review of soft computing techniques for image segmentation. It discusses various segmentation techniques including discontinuity-based techniques like point, line and edge detection using spatial filtering. Thresholding techniques like global, adaptive and multi-level thresholding are also covered. Region-based techniques such as region growing, region splitting/merging and morphological watersheds are summarized. The document concludes that future work can focus on developing genetic segmentation filters using a genetic algorithm approach for medical image segmentation.
PERFORMANCE EVALUATION OF JPEG IMAGE COMPRESSION USING SYMBOL REDUCTION TECHN...cscpconf
Lossy JPEG compression is a widely used compression technique. Normally the JPEG technique
uses two process quantization, which is lossy process and entropy encoding, which is considered
lossless process. In this paper, a new technique has been proposed by combining the JPEG
algorithm and Symbol Reduction Huffman technique for achieving more compression ratio. The
symbols reduction technique reduces the number of symbols by combining together to form a new
symbol. As a result of this technique the number of Huffman code to be generated also reduced.
The result shows that the performance of standard JPEG method can be improved by proposed method. This hybrid approach achieves about 20% more compression ratio than the Standard JPEG.
Image processing techniques can be used for face recognition applications. The process involves decomposing face images into subbands using discrete wavelet transform. The mid-frequency subband is selected and principal component analysis is applied to extract representational bases. These bases are stored for training images and used to translate probe images into representations which are classified to identify faces by matching with training representations. This approach segments discriminatory facial features to recognize identities despite variations in illumination, pose, expression and other factors.
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.
This document presents a method for image upscaling using a fuzzy ARTMAP neural network. It begins with an introduction to image upscaling and interpolation techniques. It then provides background on ARTMAP neural networks and fuzzy logic. The proposed method uses a linear interpolation algorithm trained with an ARTMAP network. Results show the method performs better than nearest neighbor interpolation in terms of peak signal-to-noise ratio, mean squared error, and structural similarity, though not as high as bicubic interpolation. Overall, the fuzzy ARTMAP network provides an effective way to perform image upscaling with fewer artifacts than traditional methods.
This document provides an overview of modern techniques for detecting video forgeries through a literature review. It discusses detecting double MPEG compression, which can identify tampering by analyzing artifacts introduced during recompression. Methods are presented for detecting duplicated frames or regions, extending image forgery detection to videos, combining artifacts across screen shots, and using multimodal feature fusion. Ghost shadow artifacts from video inpainting are also discussed as a technique for detecting forgeries. The literature review assesses these various video forgery detection methods and their applicability to different situations.
CATWALKGRADER: A CATWALK ANALYSIS AND CORRECTION SYSTEM USING MACHINE LEARNIN...mlaij
In recent years, the modeling industry has attracted many people, causing a drastic increase in the number
of modeling training classes. Modeling takes practice, and without professional training, few beginners
know if they are doing it right or not. In this paper, we present a real-time 2D model walk grading app
based on Mediapipe, a library for real-time, multi-person keypoint detection. After capturing 2D positions
of a person's joints and skeletal wireframe from an uploaded video, our app uses a scoring formula to
provide accurate scores and tailored feedback to each user for their modeling skills.
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.
In recent years due to advancement in video and image editing tools
it has become increasingly easy to modify the multimedia content. The
doctored videos are very difficult to identify through visual
examination as artifacts left behind by processing steps are subtle
and cannot be easily captured visually. Therefore, the integrity of
digital videos can no longer be taken for granted and these are not
readily acceptable as a proof-of-evidence in court-of-law. Hence,
identifying the authenticity of videos has become an important field
of information security.
In this thesis work, we present a novel approach to detect and
temporally localize video inpainting forgery based on optical flow
consistency. The proposed algorithm comprises of two stages. In the
first step, we detect if the given video is inpainted or authentic and
in the second step we perform temporal localization. Towards this, we
first compute the optical flow between frames. Further, we analyze the
goodness of fit of chi-square values obtained from optical flow
histograms using a Guassian mixture model. A threshold is then applied
to classify between authentic and inpainted videos. In the next step,
we extract Transition Probability Matrices (TPMs) by modelling the
optical flow as first order Markov process. SVM based classification
is then applied on the obtained TPM features to decide whether a block
of non-overlapping frames is authentic or inpainted thus obtaining
temporal localization. In order to evaluate the robustness of the
proposed algorithm, we perform the experiments against two popular and
efficient inpainting techniques. We test our algorithm on public
datasets like PETS and SULFA. The results show that the approach is
effective against the inpainting techniques. In addition, it detects
and localizes the inpainted frames in a video with high accuracy and
low false positives.
AN ENHANCEMENT FOR THE CONSISTENT DEPTH ESTIMATION OF MONOCULAR VIDEOS USING ...mlaij
Depth estimation has made great progress in the last few years due to its applications in robotics science
and computer vision. Various methods have been implemented and enhanced to estimate the depth without
flickers and missing holes. Despite this progress, it is still one of the main challenges for researchers,
especially for the video applications which have more complexity of the neural network which af ects the
run time. Moreover to use such input like monocular video for depth estimation is considered an attractive
idea, particularly for hand-held devices such as mobile phones, they are very popular for capturing
pictures and videos, in addition to having a limited amount of RAM. Here in this work, we focus on
enhancing the existing consistent depth estimation for monocular videos approach to be with less usage of
RAM and with using less number of parameters without having a significant reduction in the quality of the
depth estimation.
This document discusses data hiding techniques for images. It begins by introducing steganography and some common image steganography methods like LSB substitution, blocking, and palette modification. It then reviews related work on minimizing distortion in steganography, modifying matrix encoding for minimal distortion, and designing adaptive steganographic schemes. The document proposes using a universal distortion measure to evaluate embedding changes independently of the domain. It presents a system for reversible data hiding in encrypted images that partitions the image, encrypts it, hides data in the encrypted image, and allows extraction from the decrypted or encrypted image. Least significant bit substitution is discussed as an approach for hiding data in the encrypted image.
DCT based Steganographic Evaluation parameter analysis in Frequency domain by...IOSR Journals
This document analyzes DCT-based steganography using a modified JPEG luminance quantization table to improve evaluation parameters like PSNR, mean square error, and capacity. The authors propose modifying the default 8x8 quantization table by adjusting frequency values in 4 bands to increase image quality for the embedded stego image. Experimental results on test images show that using the modified table improves PSNR, decreases mean square error, and increases maximum embedding capacity compared to the default table. Therefore, the proposed method allows more secret data to be hidden with less distortion and improved image quality.
This document summarizes Diego Cheda's thesis on using monocular depth cues in computer vision applications. The thesis outlines methods for coarse depth map estimation, egomotion estimation, background estimation, and pedestrian candidate generation using monocular cues. For coarse depth map estimation, the author presents a supervised learning approach to segment images into near, medium, far, and very far depth categories using low-level visual features. Experimental results show the approach outperforms other methods using fewer features. The thesis also describes algorithms for egomotion estimation based on tracking distant regions and comparing results to other state-of-the-art methods, showing the distant region approach provides accurate rotation estimates.
Image compression and reconstruction using a new approach by artificial neura...Hưng Đặng
This document describes a neural network approach to image compression and reconstruction. It discusses using a backpropagation neural network with three layers (input, hidden, output) to compress an image by representing it with fewer hidden units than input units, then reconstructing the image from the hidden unit values. It also covers preprocessing steps like converting images to YCbCr color space, downsampling chrominance, normalizing pixel values, and segmenting images into blocks for the neural network. The neural network weights are initially randomized and then trained using backpropagation to learn the image compression.
Project Presentation presented in the final semester of B.Tech (EEE) during the session of 2010-11 By Paras Prateek Bhatnagar, Paramjeet Singh Jamwal, Nisha Rajani & Preeti Kumari.
This document outlines an introductory course on basic image processing taught by Dr. Arne Seitz at the Swiss Institute of Technology (EPFL). It discusses key topics like file formats, image viewers, representation and processing programs. Specific techniques covered include lookup tables, brightness/contrast adjustment, filtering, thresholding, and measurements. ImageJ is demonstrated as a tool for visualizing and manipulating digital images. The goal is to provide foundational concepts for working with and analyzing digital microscope images.
Dissertation synopsis for imagedenoising(noise reduction )using non local me...Arti Singh
Dissertation report for image denoising using non-local mean algorithm, discussion about subproblem of noise reduction,descrption for problem in image noise
This curriculum vitae summarizes the personal and professional information of Diogo Branco. It includes his education history with a Master's degree in Electronics and Telecommunications Engineering from the University of Aveiro in Portugal. It also lists his technical skills such as programming languages, software applications, microcontrollers, and real-time systems. Finally, it provides additional details on projects he has worked on and workshops he has attended.
Eric Chan is a freelance mobile developer based in Hong Kong with over 8 years of experience developing both native and hybrid mobile applications. He has skills in languages like Objective-C, Swift, Java, and C# as well as frameworks like Ruby on Rails, AngularJS, and PhoneGap. Eric is looking for new freelance work and provides his resume detailing his skills, experience, education, and past projects.
This document contains the resume of Ahmed Hamed Afifi, who is seeking a senior Java developer or team leader position. He has 10 years of experience in Java and J2EE programming and has worked on several projects involving technologies like Oracle ADF, SOA, web services, and databases. He is proficient in languages like Java, XML, and databases like Oracle and SQL. He has a bachelor's degree in computer science and is a Sun Certified Java Programmer.
1. The document describes several of the author's past projects including a website for a Python course built with CherryPy and Genshi that provided peptide cutting services, transplanting this site to Django, a website for a database course built with PHP and MySQL, and an internship analyzing TCGA cancer data with Python.
2. The author developed the Python course website to include input/output pages and implemented the MVC framework using Genshi templates, SQLObject for the database, and sample code in the controller and model.
3. For the database course website, the author created an EER diagram and sample pages while practicing MySQL and later updated the site with Bootstrap.
4. During their internship, the author
Color image compression based on spatial and magnitude signal decomposition IJECEIAES
In this paper, a simple color image compression system has been proposed using image signal decomposition. Where, the RGB image color band is converted to the less correlated YUV color model and the pixel value (magnitude) in each band is decomposed into 2-values; most and least significant. According to the importance of the most significant value (MSV) that influenced by any simply modification happened, an adaptive lossless image compression system is proposed using bit plane (BP) slicing, delta pulse code modulation (Delta PCM), adaptive quadtree (QT) partitioning followed by an adaptive shift encoder. On the other hand, a lossy compression system is introduced to handle the least significant value (LSV), it is based on an adaptive, error bounded coding system, and it uses the DCT compression scheme. The performance of the developed compression system was analyzed and compared with those attained from the universal standard JPEG, and the results of applying the proposed system indicated its performance is comparable or better than that of the JPEG standards.
This document provides an exploratory review of soft computing techniques for image segmentation. It discusses various segmentation techniques including discontinuity-based techniques like point, line and edge detection using spatial filtering. Thresholding techniques like global, adaptive and multi-level thresholding are also covered. Region-based techniques such as region growing, region splitting/merging and morphological watersheds are summarized. The document concludes that future work can focus on developing genetic segmentation filters using a genetic algorithm approach for medical image segmentation.
PERFORMANCE EVALUATION OF JPEG IMAGE COMPRESSION USING SYMBOL REDUCTION TECHN...cscpconf
Lossy JPEG compression is a widely used compression technique. Normally the JPEG technique
uses two process quantization, which is lossy process and entropy encoding, which is considered
lossless process. In this paper, a new technique has been proposed by combining the JPEG
algorithm and Symbol Reduction Huffman technique for achieving more compression ratio. The
symbols reduction technique reduces the number of symbols by combining together to form a new
symbol. As a result of this technique the number of Huffman code to be generated also reduced.
The result shows that the performance of standard JPEG method can be improved by proposed method. This hybrid approach achieves about 20% more compression ratio than the Standard JPEG.
Image processing techniques can be used for face recognition applications. The process involves decomposing face images into subbands using discrete wavelet transform. The mid-frequency subband is selected and principal component analysis is applied to extract representational bases. These bases are stored for training images and used to translate probe images into representations which are classified to identify faces by matching with training representations. This approach segments discriminatory facial features to recognize identities despite variations in illumination, pose, expression and other factors.
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.
This document presents a method for image upscaling using a fuzzy ARTMAP neural network. It begins with an introduction to image upscaling and interpolation techniques. It then provides background on ARTMAP neural networks and fuzzy logic. The proposed method uses a linear interpolation algorithm trained with an ARTMAP network. Results show the method performs better than nearest neighbor interpolation in terms of peak signal-to-noise ratio, mean squared error, and structural similarity, though not as high as bicubic interpolation. Overall, the fuzzy ARTMAP network provides an effective way to perform image upscaling with fewer artifacts than traditional methods.
This document provides an overview of modern techniques for detecting video forgeries through a literature review. It discusses detecting double MPEG compression, which can identify tampering by analyzing artifacts introduced during recompression. Methods are presented for detecting duplicated frames or regions, extending image forgery detection to videos, combining artifacts across screen shots, and using multimodal feature fusion. Ghost shadow artifacts from video inpainting are also discussed as a technique for detecting forgeries. The literature review assesses these various video forgery detection methods and their applicability to different situations.
CATWALKGRADER: A CATWALK ANALYSIS AND CORRECTION SYSTEM USING MACHINE LEARNIN...mlaij
In recent years, the modeling industry has attracted many people, causing a drastic increase in the number
of modeling training classes. Modeling takes practice, and without professional training, few beginners
know if they are doing it right or not. In this paper, we present a real-time 2D model walk grading app
based on Mediapipe, a library for real-time, multi-person keypoint detection. After capturing 2D positions
of a person's joints and skeletal wireframe from an uploaded video, our app uses a scoring formula to
provide accurate scores and tailored feedback to each user for their modeling skills.
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.
In recent years due to advancement in video and image editing tools
it has become increasingly easy to modify the multimedia content. The
doctored videos are very difficult to identify through visual
examination as artifacts left behind by processing steps are subtle
and cannot be easily captured visually. Therefore, the integrity of
digital videos can no longer be taken for granted and these are not
readily acceptable as a proof-of-evidence in court-of-law. Hence,
identifying the authenticity of videos has become an important field
of information security.
In this thesis work, we present a novel approach to detect and
temporally localize video inpainting forgery based on optical flow
consistency. The proposed algorithm comprises of two stages. In the
first step, we detect if the given video is inpainted or authentic and
in the second step we perform temporal localization. Towards this, we
first compute the optical flow between frames. Further, we analyze the
goodness of fit of chi-square values obtained from optical flow
histograms using a Guassian mixture model. A threshold is then applied
to classify between authentic and inpainted videos. In the next step,
we extract Transition Probability Matrices (TPMs) by modelling the
optical flow as first order Markov process. SVM based classification
is then applied on the obtained TPM features to decide whether a block
of non-overlapping frames is authentic or inpainted thus obtaining
temporal localization. In order to evaluate the robustness of the
proposed algorithm, we perform the experiments against two popular and
efficient inpainting techniques. We test our algorithm on public
datasets like PETS and SULFA. The results show that the approach is
effective against the inpainting techniques. In addition, it detects
and localizes the inpainted frames in a video with high accuracy and
low false positives.
AN ENHANCEMENT FOR THE CONSISTENT DEPTH ESTIMATION OF MONOCULAR VIDEOS USING ...mlaij
Depth estimation has made great progress in the last few years due to its applications in robotics science
and computer vision. Various methods have been implemented and enhanced to estimate the depth without
flickers and missing holes. Despite this progress, it is still one of the main challenges for researchers,
especially for the video applications which have more complexity of the neural network which af ects the
run time. Moreover to use such input like monocular video for depth estimation is considered an attractive
idea, particularly for hand-held devices such as mobile phones, they are very popular for capturing
pictures and videos, in addition to having a limited amount of RAM. Here in this work, we focus on
enhancing the existing consistent depth estimation for monocular videos approach to be with less usage of
RAM and with using less number of parameters without having a significant reduction in the quality of the
depth estimation.
This document discusses data hiding techniques for images. It begins by introducing steganography and some common image steganography methods like LSB substitution, blocking, and palette modification. It then reviews related work on minimizing distortion in steganography, modifying matrix encoding for minimal distortion, and designing adaptive steganographic schemes. The document proposes using a universal distortion measure to evaluate embedding changes independently of the domain. It presents a system for reversible data hiding in encrypted images that partitions the image, encrypts it, hides data in the encrypted image, and allows extraction from the decrypted or encrypted image. Least significant bit substitution is discussed as an approach for hiding data in the encrypted image.
DCT based Steganographic Evaluation parameter analysis in Frequency domain by...IOSR Journals
This document analyzes DCT-based steganography using a modified JPEG luminance quantization table to improve evaluation parameters like PSNR, mean square error, and capacity. The authors propose modifying the default 8x8 quantization table by adjusting frequency values in 4 bands to increase image quality for the embedded stego image. Experimental results on test images show that using the modified table improves PSNR, decreases mean square error, and increases maximum embedding capacity compared to the default table. Therefore, the proposed method allows more secret data to be hidden with less distortion and improved image quality.
This document summarizes Diego Cheda's thesis on using monocular depth cues in computer vision applications. The thesis outlines methods for coarse depth map estimation, egomotion estimation, background estimation, and pedestrian candidate generation using monocular cues. For coarse depth map estimation, the author presents a supervised learning approach to segment images into near, medium, far, and very far depth categories using low-level visual features. Experimental results show the approach outperforms other methods using fewer features. The thesis also describes algorithms for egomotion estimation based on tracking distant regions and comparing results to other state-of-the-art methods, showing the distant region approach provides accurate rotation estimates.
Image compression and reconstruction using a new approach by artificial neura...Hưng Đặng
This document describes a neural network approach to image compression and reconstruction. It discusses using a backpropagation neural network with three layers (input, hidden, output) to compress an image by representing it with fewer hidden units than input units, then reconstructing the image from the hidden unit values. It also covers preprocessing steps like converting images to YCbCr color space, downsampling chrominance, normalizing pixel values, and segmenting images into blocks for the neural network. The neural network weights are initially randomized and then trained using backpropagation to learn the image compression.
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9
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1. Samer Sawalha & Arafat Awajan
International Journal of Image Processing (IJIP), Volume (8) : Issue (1) : 2014 9
Blank Background Image lossless Compression Technique
Samer Sawalha samerfs@hotmail.com
Information and Communication Technology
Royal Scientific Society
Amman-Jordan
Arafat Awajan awajan@psut.edu.jo
King Hussein School for Computing Sciences
Princess Sumaya University for Technology
Amman-Jordan
Abstract
This paper presents a new technique able to provide a very good compression ratio in preserving
the quality of the important components of the image called main objects. It focuses on
applications where the image is of large size and consists of an object or a set of objects on
background such as identity photos. In these applications, the background of the objects is in
general uniform and represents insignificant information for the application. The results of this
new techniques show that is able to achieve an average compression ratio of 29% without any
degradation of the quality of objects detected in the images. These results are better than the
results obtained by the lossless techniques such as JPEG and TIF techniques.
Keywords: Image Compression, Blank Background Images, Histogram Based Thresholding,
Background Detection, Object Detection.
1. INTRODUCTION
Storing huge amount of high quality images may cause storage capacity problems and speed
down their access. Image compression is the process of representing an image with fewer bits
while maintaining image quality, saving cost associated with sending less data over
communication lines and finally reducing the probability of transmission errors [5].
There are many previous studies and techniques which have dealt with compressing images.
These techniques are grouped into two classes: the lossy compression techniques and the
lossless compression techniques. The lossless compression algorithms save the quality and
information but their performance in term of compression ratio and time access are weak. The
lossy compression algorithms destroy the quality, crop the image, change its dimensions or
remove some important information from it.
Applications such as Civil Affairs Department information system (CAD) and Human Resources
information systems manipulate huge amount of pictorial information. These applications store
the images either in separate files or in databases using Oracle, MySQL, SQL Server. These
applications face problems of storage capacity and access time to these images due to the huge
amount of high quality images sizes [1] [2] [3]. The main objective of this work is to develop a
technique to store large sized images. This technique should preserve the original dimensions of
the image and its resolution, and decrease its size. We are focusing on applications where the
image background does not carry important information since the critical information is only the
object or objects on the background of the image. The face component in an identity photos in
CAD application is an example of these application.
2. Samer Sawalha & Arafat Awajan
International Journal of Image Processing (IJIP), Volume (8) : Issue (1) : 2014 10
2. PREVIOUS STUDIES
During the past decades, a lot of studies were conducted on image compression. These
researches succeeded to provide good number of compression techniques able to reduce the
size of the images. Each one of these works has its own objectives, advantages and
disadvantages. In this section, we will discuss some of these compressions techniques [4]. The
image compression techniques are broadly classified into two categories depending on whether if
we can restore the exact replica of the original or not. These categories are the lossless image
compression techniques and the lossy image compression techniques [6].
2.1 Lossless Image Compression
Lossless image compression techniques allow the image to be encoded (compressed) into a
smaller size without losing any details of the image. The compressed image can then be decoded
in order to retrieve the original image [6]. The main techniques belonging to this category are
RLE, LZW and Huffman encoding.
2.1.1 Run Length Encoding (RLE)
It is a simple and direct compression technique. It takes advantage of the fact that lot of adjacent
pixels are often identical in the image [3]. RLE checks the stream of pixels and inserts a special
token each time a chain of more than two equal adjacent pixels value are found. This special
input advises the decoder to insert the following pixel value n times into its output stream [7]. RLE
can be easily implemented, either in software or in hardware. It is fast and very well verifiable
However, its compression performances are very limited mainly when the lighting of the image
produce makes some kind of gradient.
2.1.2 Lempel-Ziv-Welch (LZW) Coding
It assigns fixed length code words to variable length sequences of coding symbols without a priori
knowledge of the symbols probabilities. At the onset of the coding process, a dictionary
containing the source symbols to be coded is constructed, for 8-bit monochrome images, the first
256 words of the dictionary are assigned to intensities 0, 1,255 [1]. As the encoder sequentially
examines image pixels, intensity sequences that are not in the dictionary are placed in
determined locations. For instance, if the first two pixels are white, sequence “255-255” might be
assigned to 256. Next time – when two consecutive white pixels are encountered – code word
256 will be used to represent them [1].
2.1.3 Huffman Encoding
This technique is based on the statistical occurrence of symbols. In image compression, the
pixels in the image are treated as symbols. The symbols that occur more frequently are assigned
a smaller number of bits, while the symbols that occur less frequently are assigned a relatively
larger number of bits. Huffman code is a prefix code. This means that the binary code of any
symbol is not the prefix of the code of any other symbol. Most image coding standards use lossy
techniques in the earlier stages of compression and use Huffman coding as the final step [3].
2.2 Lossy Image Compression
The lossy image compression methods are better in compression ratio than the lossless
methods. However, the generated image will not be same as original image: some of information
may disappear and the quality may decrease [6]. These techniques are widely used in application
where the size of the image and speed of transmission is more critical than the quality of the
image. There are many examples of lossy image compression methods such as JPEG [8],
JPEG2000 [8] and Transformation coding [3].
2.2.1 JPEG
Is created by the Joint Photographic Experts Group in 1983 and adopted by both the International
Standards Organization (ISO) and International Telegraph Union Telecommunications standards
sector (ITU-T). It is the most commonly used method for compressing digital images.
3. Samer Sawalha & Arafat Awajan
International Journal of Image Processing (IJIP), Volume (8) : Issue (1) : 2014 11
JPEG reduces the size of an image by breaking it into 8x8 blocks and within each block, shifting
and simplifying the colours so there is less information to encode. The pixels are changed only in
relation to the other pixels within their block, two identical pixels that are next to each other, but in
different blocks, could be transformed into different values. When high compression ratios are
used, the block boundaries become obvious, causing the 'blockiness' or 'blocking artifacts’
frequently observed in common JPEG [8].
2.2.2 JPEG2000
It uses state-of-the-art compression techniques based on wavelet technology. The JPEG2000
standard was adopted by ISO. It is a wavelet-based compression algorithm that offers superior
compression performance over JPEG at moderate compression ratios. The end result is a much
better image quality however at high levels of compression the image appears blurred [8].
2.2.3 Transformation Coding
In this coding scheme, transformations such as Discrete Fourier Transform DFT and Discrete
Cosine Transform DCT are used to transform the pixels in the original image into frequency
domain coefficients. These coefficients have several desirable properties such as the energy
compaction property. The results are concentrated in only a few of the significant transform
coefficients. This is the basis of achieving the compression. Only those few significant coefficients
are selected and the remaining is discarded. The selected coefficients are considered for further
quantization and entropy encoding. DCT coding has been the most common used approach; it is
also adopted in the JPEG image compression standard [3].
3. IMAGE COMPRESSION
Our proposed technique consists of a sequence of operations aiming at determining the
background of the image, extracting identifying objects and compressing the image. These
operations can be described by the following algorithm:
a) Transform the image into Grayscale image.
b) Histogram generation
c) Threshold calculation
d) Average background colour selection
e) Search for an object in the image
f) Object extraction
g) Image object compression
h) Object subtraction
i) Go to 5 until no objects are found
j) Store extracted objects and background colour
3.1 Grayscale Image Generation
The manipulated images are in general coloured image where each pixel is represented by three
values representing the three main components or colours: Red, green and blue. The proposed
technique starts by transforming the colour image into grayscale image. A grayscale image is an
image where each pixel has a single value representing the intensity or brightness information at
that pixel. The range of each pixel value is between 0 and 255 if we associate one byte for each
pixel.
The luminosity method which is a more sophisticated version of the average method is used in
this work to generate the grayscale image. It calculates a weighted average of luminosity
according to the formula (0.21 Red value + 0.71 Green value + 0.07 Blue value).
3.2 Background Extraction
In many applications, the gray levels of pixels belonging to the object are generally different from
the gray levels of the pixels belonging to the background. This fact is a general features in
applications such as identity photos in CAD applications. Thus, thresholding becomes a simple
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International Journal of Image Processing (IJIP), Volume (8) : Issue (1) : 2014 12
but effective tool to separate objects from the background [9]. The selection of a threshold value
is then a critical operation preceding the extraction of the background.
Image thresholding is one of the most important techniques for image segmentation aiming at
partitioning an image into homogeneous regions, background and main objects in our case. It is
regarded as an analytic image presentation method and plays a very important role in many tasks
of pattern recognition, computer vision, and image and video retrieval [1].
There are two main groups of thresholding algorithms: Fixed threshold and histogram derived
threshold. In the fixed threshold selection, the threshold is chosen independently of the image
data. In the histogram derived threshold selection, the threshold is selected from the brightness
histogram of the region or image that we wish to segment. Since the images are different in
colour, brightness, and applications, the histogram based histogram is adopted in this work.
There are many methods for selecting threshold using image histogram such as Ridler and
Calvard algorithm, Tsai algorithm, Otsu algorithm, Kapur et al. algorithm, Rosin algorithm, and the
Triangle algorithm. In this work, the triangle threshold algorithm is used because almost all the
blank background image have a clear peak, so we can select the threshold using this algorithm
easily and give accurate values. Another reason for using this algorithm, sometimes the
background of the image is darker than the object itself, so by detecting the slope of the
hypotenuse of the triangle we will know which is darker the objects or the background.
A line is constructed between the maximum value of the histogram (peak) and the lowest value.
The distance d between the line and the histogram h[b] is computed for all values of b from b =
bmin to b = bmax. The brightness value b0 where the distance between h and the line is maximal
is the threshold value. This technique is particularly effective when the object pixels produce a
weak peak in the histogram as shown in Figure 1. We consider that the most repeated colour
over the blank background image is the background colour.
3.3 Object Boundaries Detection
The slope of hypotenuse in the triangle threshold algorithm is used to determine the pixels
belonging to the background. If the slope is positive, the object is darker than the background and
the background consists of pixel with values greater than the threshold and the other pixels will
be considered as objects points, and vice versa.
FIGURE 1: Triangle Thresholding Algorithm [10].
The object detection is done by scanning the image from top to bottom, left to right until we reach
a point belonging to an object. Then, we stop scanning and start detecting the contour of the
object by checking the eight adjacent pixels of the current pixel to move to the next point of the
border.
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International Journal of Image Processing (IJIP), Volume (8) : Issue (1) : 2014 13
After comparing the next pixel with the threshold, if this pixel does not belong to the object then
go to the next direction in counter clockwise direction as shown in Figure 2; else add this pixel as
a part of the object and move the current pixel to it. This operation continues until we reach the
starting point.
FIGURE 2: Boundary Tracing in 8-Connectivity [11].
3.4 Object Extraction
The previous step deals with the grayscale image and the threshold. It returns an array of points
(closed area) representing the border of the object. Using the detected contours points, we crop
the corresponding object from the original image. The detected object is extracted from the
original image and then is stored as a transparent image which has only the detected object.
3.5 Image Object Compression
The new image with the extracted object is compressed using the JPEG lossless compression
technique. This allows us to have good compression ratio without changing the quality of the
object detected in the previous step. Since there is no transparency in the JPEG compression, we
fill the transparent part in the image with the background colour which we had selected in the
previous steps.
3.6 Multiple Objects Detection (Object Subtraction)
The image may contain more than one object, so we need to remove the found object from the
original image and rescan the image for new objects. Removing the detected object is done by
replacing its pixels values by the background colour selected from the previous steps without
changing the dimension of the original image. Then, we repeat the previous steps starting from
searching for an object in the image, object extraction, image object compression until no objects
found in the original image.
3.7 Compressed File Structure
The compressed image generated from the previous steps is then stored in file that has two main
parts: header and body. The header of the file contains general information about the image such
as average background colour, width and height of original image, the position of each object
given by its top left point, its array size and the number of objects extracted.
The body of the file includes the objects extracted. It contains the information about each object
detected stored as an array of bytes that will be converted into image in the image
decompressing phase.
3.8 Process Scenario
In this chapter we will make an example on our technique by choosing an image; this image is a
human image as shown in figure 3 (a), first of all we detect the contour of the main object as
shown in figure 3 (b), the main object is the human himself, then we extract the main object from
the image so it will result two new images the background and the main object as in figure 3 (c),
figure 3 (d) then we compress the image in figure 3 (d), we choose the average background color,
after that we store all these information as a text as shown in figure 3 (e), this text is considered
as the final output from the compressing process.
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International Journal of Image Processing (IJIP), Volume (8) : Issue (1) : 2014 14
After we compressed the file we need to retrieve the image (decompress the stored file), in figure
3 (f) show the image after decompression process, it is same as original in height, width and the
main object, the only deference is the background color is shown as one solid color.
FIGURE 3: Example Scenario.
4. ALGORITHM EVALUATION
An evaluation of the abilities and the features of this technique are presented in this section. The
evaluation aims at comparing the results of the proposed technique with other compression
techniques in term of quality, compression ratio and time.
4.1 Dataset
To test this application we used a large set of images from the student’s registration department
in Princess Sumaya University for Technology. Because this kind of applications considers the
background as irrelevant information.
The dataset consists of 1000 student images of type bitmap BMP. They contain mainly the face
of the student (the main object) and a blank background with different colours, the maximum file
size is 0.926 megabytes and the minimum size is 0.111 megabytes, with a total size 317
megabytes.
4.2 Benchmarks
We will evaluate our system in term of the following benchmarks:
• The quality of the significant information of the image (the main objects in the image)
• Compression ratio (the size of generated image after executing the compression
comparing with the original image).
• The time elapsed to compress the images.
• The time elapsed to decompress the images.
The proposed compression technique is compared with the following compression algorithms:
Lossless compression using JPEG algorithm, Lossless compression using LZW algorithm with
TIF file format and Lossy compression using GIF algorithm. These techniques are chosen based
on their popularity and their effectiveness in all types of images.
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4.3. Results
4.3.1 Quality of Main Objects In The Image
We compared the resulted images after compression process with the original images using an
application that checks each pixel in the original image with the same pixel in the compressed
image; the results are shown in table 1. We noticed that the quality of the main objects in the
image is preserved in the JPEG, TIF, and in our system. But when we used the GIF compression
the quality is reduced since it is lossy compression.
JPEG TIF GIF Proposed Technique
Yes Yes No Yes
TABLE 1: Quality of Compressed Main Objects.
4.3.2 Compression ratio
The compression ratio (CR) measure the amount of compression realized. Different definitions
are available in the literature. We considered the following definition for the purpose of evaluation:
CR=(Compressed image size/ Original image size)*100%
Table 2 shows the size of dataset images before and after compressing the images using the
different compression techniques. Table 3 highlight the compression ratio obtained from the
different techniques applied on the 1000 image of the dataset.
Number
of
images
BMP JPEG TIF GIF
Proposed
Technique
10 3.133 2.117 2.564 0.548 0.884
100 31.105 21.094 25.452 5.361 8.568
500 159.694 108.761 131.497 27.308 44.957
1000 317.198 214.327 259.248 54.235 91.328
TABLE 2: Compressed Images Sizes in MB.
JPEG TIF GIF
Proposed
Technique
67.5% 81% 17% 29%
TABLE 3: Compression Ratio Evaluation.
The GIF algorithm got the best compression ratio because it is a lossy compression, the quality of
the compressed image is not the same as the original image and there is some information lost.
The proposed technique got the best results over the lossless techniques JPEG and the TIF
techniques, because our algorithm eliminate the background then compress the image of main
objects with the lossless JPEG compression. Thus, the amount of data needed to represent the
image is less than the JPEG and the TIF algorithms which store all the image information
including the with background details.
4.3.3 Compression Time
The time elapsed to compress the 1000 images is different according to the algorithm used. Our
algorithm needed more time than the other techniques. It consume more time in the preliminary
steps such as: the grayscale image generation, the histogram generation, the average
background selection, object detection. As this cost is paid only once, the other metrics mainly
the compression ratio favorites the proposed technique.
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International Journal of Image Processing (IJIP), Volume (8) : Issue (1) : 2014 16
5. CONCLUSION
A technique for compressing the image is proposed and evaluated in this paper. It saves the
quality of the main components of the image and achieves good results in term of compression
ratio. This technique is adequate for applications where the background is not important in the
image.
Image compression is very important for image storage and transmission, so as future work we
plan to experiment our system under different environments, illumination change, variable noise
levels, and study the effects of image pre- and post-filtering. Special cases such as images with
background colour very close to the objects colour need to be investigated.
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