n this paper, to detect the moving objects between frames in compressed video and to obtain the bes
t
compression video
and the noiseless video. We describe a video in which frames by classifying
macroblocks (MB), and describe motion estimation (ME), motion vector field (MV) and motion
compensation (MC). we propose to classify Macroblocks of each video frame into different
classes and use
this class information to describe the frame content based on the motion vector. MB class informatio
n
video applications such as shot change detection, motion discontinuity detection, Outlier rejection
for
global motion estimation. To reduc
e the noise and to improve the clarity of the compressed video by using
contrast limited adaptive histogram equalization (CLAHE) Algorithm.
Blur Detection Methods for Digital Images-A SurveyEditor IJCATR
This paper described various blur detection methods along with proposed method. Digital photos are massively produced
while digital cameras are becoming popular; however, not every photo has good quality. Blur is one of the conventional image quality
degradation which is caused by various factors like limited contrast; inappropriate exposure time and improper device handling indeed,
blurry images make up a significant percentage of anyone's picture collections. Consequently, an efficient tool to detect blurry images
and label or separate them for automatic deletion in order to preserve storage capacity and the quality of image collections is needed.
There are various methods to detect the blur from the blurry images some of which requires transforms like DCT or Wavelet and some
doesn‟t require transform.
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.
Digital video is, a sequence of images, called frames, displayed at a certain frame rate (so many frames per second, or fps) to create the illusion of animation.
Introduction to Digital Videos, Motion Estimation: Principles & Compensation. Learn more in IIT Kharagpur's Image and Video Communication online certificate course.
NEW IMPROVED 2D SVD BASED ALGORITHM FOR VIDEO CODINGcscpconf
Video compression is one of the most important blocks of an image acquisition system.
Compression of video results in reduction of transmission bandwidth. In real time video
compression the incoming video data is directly compressed without being stored first.
Therefore real time video compression system operates under stringent timing constraints.
Current video compression standards like MPEG, H.26x series, involve emotion estimation and
compensation blocks which are highly computationally expensive and hence they are not
suitable for real time applications on resource scarce systems. Current applications like video
calling, video conferencing require low complexity video compression algorithms so that they
can be implemented in environments that have scarce computational resources (like mobile
phones). A low complexity video compression algorithm based on 2D SVD exists. In this paper, a modification to that algorithm which provides higher PSNR at the same bit rate is presented.
Blur Detection Methods for Digital Images-A SurveyEditor IJCATR
This paper described various blur detection methods along with proposed method. Digital photos are massively produced
while digital cameras are becoming popular; however, not every photo has good quality. Blur is one of the conventional image quality
degradation which is caused by various factors like limited contrast; inappropriate exposure time and improper device handling indeed,
blurry images make up a significant percentage of anyone's picture collections. Consequently, an efficient tool to detect blurry images
and label or separate them for automatic deletion in order to preserve storage capacity and the quality of image collections is needed.
There are various methods to detect the blur from the blurry images some of which requires transforms like DCT or Wavelet and some
doesn‟t require transform.
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.
Digital video is, a sequence of images, called frames, displayed at a certain frame rate (so many frames per second, or fps) to create the illusion of animation.
Introduction to Digital Videos, Motion Estimation: Principles & Compensation. Learn more in IIT Kharagpur's Image and Video Communication online certificate course.
NEW IMPROVED 2D SVD BASED ALGORITHM FOR VIDEO CODINGcscpconf
Video compression is one of the most important blocks of an image acquisition system.
Compression of video results in reduction of transmission bandwidth. In real time video
compression the incoming video data is directly compressed without being stored first.
Therefore real time video compression system operates under stringent timing constraints.
Current video compression standards like MPEG, H.26x series, involve emotion estimation and
compensation blocks which are highly computationally expensive and hence they are not
suitable for real time applications on resource scarce systems. Current applications like video
calling, video conferencing require low complexity video compression algorithms so that they
can be implemented in environments that have scarce computational resources (like mobile
phones). A low complexity video compression algorithm based on 2D SVD exists. In this paper, a modification to that algorithm which provides higher PSNR at the same bit rate is presented.
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.
Efficient Image Compression Technique using JPEG2000 with Adaptive ThresholdCSCJournals
Image compression is a technique to reduce the size of image which is helpful for transforms. Due to the limited communication bandwidth we have to need optimum compressed image with good visual quality. Although the JPEG2000 compression technique is ideal for image processing as it uses DWT (Discrete Wavelet Transform).But in this paper we proposed fast and efficient image compression scheme using JPEG2000 technique with adaptive subband threshold. Actually we used subband adaptive threshold in decomposition section which gives us more compression ratio and good visual quality other than existing compression techniques. The subband adaptive threshold that concentrates on denoising each subband (except lowest coefficient subbands) by minimizing insignificant coefficients and adapt with modified coefficients which are significant and more responsible for image reconstruction. Finally we use embedded block coding with optimized truncation (EBCOT) entropy coder that gives three different passes which gives more compressed image. This proposed method is compared to other existing approach and give superior result that satisfy the human visual quality and also these resulting compressed images are evaluated by the performance parameter PSNR.
High Performance Architecture for Full Search Block matching Algorithmiosrjce
Video compression has two major issues to be handled, one is video compression rate and other one
is quality. There is always a trade-off between speed and quality. Full search block matching algorithm
(FSBMA) is most popular motion estimation algorithm. But high computational complexity is the major
challenge of FSBM. This makes FSBM to be very difficult to use for real time video processing with the low
power batteries. Other algorithm gives better speed on the expense of quality of video. The proposed algorithm
i.e. modified full search block matching algorithm (MFSBMA) reduces the computational complexity by keeping
the PSNR same as of FSBMA. MFSBMA skips the SAD calculations for a current background macroblock and it
does SAD calculations for foreground current microblock. This method reduces SAD calculations drastically.
This work presents apipelined architecture forMFSBMA which can work on real time HDTV video processing.
The proposed algorithm reduces computational complexity by 50% keeping PSNR same with the full search
algorithm.
Video Shot Boundary Detection Using The Scale Invariant Feature Transform and...IJECEIAES
Segmentation of the video sequence by detecting shot changes is essential for video analysis, indexing and retrieval. In this context, a shot boundary detection algorithm is proposed in this paper based on the scale invariant feature transform (SIFT). The first step of our method consists on a top down search scheme to detect the locations of transitions by comparing the ratio of matched features extracted via SIFT for every RGB channel of video frames. The overview step provides the locations of boundaries. Secondly, a moving average calculation is performed to determine the type of transition. The proposed method can be used for detecting gradual transitions and abrupt changes without requiring any training of the video content in advance. Experiments have been conducted on a multi type video database and show that this algorithm achieves well performances.
To get this project in ONLINE or through TRAINING Sessions,
Contact:JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83. Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry -9. Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: jpinfotechprojects@gmail.com, web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com
Neural network based image compression with lifting scheme and rlceSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Single Image Depth Estimation using frequency domain analysis and Deep learningAhan M R
Using Machine Learning and Deep Learning Techniques, we train the ResNet CNN Model and build a model for estimating Depth using the Discrete Fourier Domain Analysis, and generate results including the explanation of the Loss function and code snippets.
Fpga implementation of fusion technique for fingerprint applicationIAEME Publication
Image Fusion is a process of combining relevant information from a set of images, into a
single image, wherein the resultant fused image will be more informative and complete than any of
the input images. This paper discusses Laplacian Pyramid (LP) based image fusion techniques for
fingerprint application. The technique is implemented in MatLab and evaluation parameters Mean
Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Matching score are discussed. As well
the same implemented on Virtex-5 FPGA development board using Verilog HDL. LP based
technique provides better results for image fusion than other 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.
Efficient Architecture for Variable Block Size Motion Estimation in H.264/AVCIDES Editor
This paper proposes an efficient VLSI architecture
for the implementation of variable block size motion
estimation (VBSME). To improve the performance video
compression the Variable Block Size Motion Estimation
(VBSME) is the critical path. Variable Block Size Motion
Estimation feature has been introduced in to the H.264/AVC.
This feature induces significant complexities into the design
of the H.264/AVC video codec. This paper we compare the
existing architectures for VBSME. An efficient architecture
to improve the performance of Spiral Search for Variable Size
Motion Estimation in H.264/AVC is proposed. Among various
architectures available for VBSME spiral search provides
hardware friendly data flow with efficient utilization of
resources. The proposed implementation is verified using the
MATLAB on foreman, coastguard and train sequences. The
proposed Adaptive thresholding technique reduces the average
number of computations significantly with negligible effect
on the video quality. The results are verified using hardware
implementation on Xilinx Virtex 4 it was able to achieve real
time video coding of 60 fps at 95.56 MHz CLK frequency.
Internet data almost double every year. The need of multimedia communication
is less storage space and fast transmission. So, the large volume of video data has become
the reason for video compression. The aim of this paper is to achieve temporal compression
for three-dimensional (3D) videos using motion estimation-compensation and wavelets.
Instead of performing a two-dimensional (2D) motion search, as is common in conventional
video codec’s, the use of a 3D motion search has been proposed, that is able to better exploit
the temporal correlations of 3D content. This leads to more accurate motion prediction and
a smaller residual. The discrete wavelet transform (DWT) compression scheme has been
added for better compression ratio. The DWT has a high-energy compaction property thus
greatly impacted the field of compression. The quality parameters peak signal to noise ratio
(PSNR) and mean square error (MSE) have been calculated. The simulation results shows
that the proposed work improves the PSNR from existing work.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
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.
Efficient Image Compression Technique using JPEG2000 with Adaptive ThresholdCSCJournals
Image compression is a technique to reduce the size of image which is helpful for transforms. Due to the limited communication bandwidth we have to need optimum compressed image with good visual quality. Although the JPEG2000 compression technique is ideal for image processing as it uses DWT (Discrete Wavelet Transform).But in this paper we proposed fast and efficient image compression scheme using JPEG2000 technique with adaptive subband threshold. Actually we used subband adaptive threshold in decomposition section which gives us more compression ratio and good visual quality other than existing compression techniques. The subband adaptive threshold that concentrates on denoising each subband (except lowest coefficient subbands) by minimizing insignificant coefficients and adapt with modified coefficients which are significant and more responsible for image reconstruction. Finally we use embedded block coding with optimized truncation (EBCOT) entropy coder that gives three different passes which gives more compressed image. This proposed method is compared to other existing approach and give superior result that satisfy the human visual quality and also these resulting compressed images are evaluated by the performance parameter PSNR.
High Performance Architecture for Full Search Block matching Algorithmiosrjce
Video compression has two major issues to be handled, one is video compression rate and other one
is quality. There is always a trade-off between speed and quality. Full search block matching algorithm
(FSBMA) is most popular motion estimation algorithm. But high computational complexity is the major
challenge of FSBM. This makes FSBM to be very difficult to use for real time video processing with the low
power batteries. Other algorithm gives better speed on the expense of quality of video. The proposed algorithm
i.e. modified full search block matching algorithm (MFSBMA) reduces the computational complexity by keeping
the PSNR same as of FSBMA. MFSBMA skips the SAD calculations for a current background macroblock and it
does SAD calculations for foreground current microblock. This method reduces SAD calculations drastically.
This work presents apipelined architecture forMFSBMA which can work on real time HDTV video processing.
The proposed algorithm reduces computational complexity by 50% keeping PSNR same with the full search
algorithm.
Video Shot Boundary Detection Using The Scale Invariant Feature Transform and...IJECEIAES
Segmentation of the video sequence by detecting shot changes is essential for video analysis, indexing and retrieval. In this context, a shot boundary detection algorithm is proposed in this paper based on the scale invariant feature transform (SIFT). The first step of our method consists on a top down search scheme to detect the locations of transitions by comparing the ratio of matched features extracted via SIFT for every RGB channel of video frames. The overview step provides the locations of boundaries. Secondly, a moving average calculation is performed to determine the type of transition. The proposed method can be used for detecting gradual transitions and abrupt changes without requiring any training of the video content in advance. Experiments have been conducted on a multi type video database and show that this algorithm achieves well performances.
To get this project in ONLINE or through TRAINING Sessions,
Contact:JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83. Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry -9. Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: jpinfotechprojects@gmail.com, web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com
Neural network based image compression with lifting scheme and rlceSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Single Image Depth Estimation using frequency domain analysis and Deep learningAhan M R
Using Machine Learning and Deep Learning Techniques, we train the ResNet CNN Model and build a model for estimating Depth using the Discrete Fourier Domain Analysis, and generate results including the explanation of the Loss function and code snippets.
Fpga implementation of fusion technique for fingerprint applicationIAEME Publication
Image Fusion is a process of combining relevant information from a set of images, into a
single image, wherein the resultant fused image will be more informative and complete than any of
the input images. This paper discusses Laplacian Pyramid (LP) based image fusion techniques for
fingerprint application. The technique is implemented in MatLab and evaluation parameters Mean
Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Matching score are discussed. As well
the same implemented on Virtex-5 FPGA development board using Verilog HDL. LP based
technique provides better results for image fusion than other 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.
Efficient Architecture for Variable Block Size Motion Estimation in H.264/AVCIDES Editor
This paper proposes an efficient VLSI architecture
for the implementation of variable block size motion
estimation (VBSME). To improve the performance video
compression the Variable Block Size Motion Estimation
(VBSME) is the critical path. Variable Block Size Motion
Estimation feature has been introduced in to the H.264/AVC.
This feature induces significant complexities into the design
of the H.264/AVC video codec. This paper we compare the
existing architectures for VBSME. An efficient architecture
to improve the performance of Spiral Search for Variable Size
Motion Estimation in H.264/AVC is proposed. Among various
architectures available for VBSME spiral search provides
hardware friendly data flow with efficient utilization of
resources. The proposed implementation is verified using the
MATLAB on foreman, coastguard and train sequences. The
proposed Adaptive thresholding technique reduces the average
number of computations significantly with negligible effect
on the video quality. The results are verified using hardware
implementation on Xilinx Virtex 4 it was able to achieve real
time video coding of 60 fps at 95.56 MHz CLK frequency.
Internet data almost double every year. The need of multimedia communication
is less storage space and fast transmission. So, the large volume of video data has become
the reason for video compression. The aim of this paper is to achieve temporal compression
for three-dimensional (3D) videos using motion estimation-compensation and wavelets.
Instead of performing a two-dimensional (2D) motion search, as is common in conventional
video codec’s, the use of a 3D motion search has been proposed, that is able to better exploit
the temporal correlations of 3D content. This leads to more accurate motion prediction and
a smaller residual. The discrete wavelet transform (DWT) compression scheme has been
added for better compression ratio. The DWT has a high-energy compaction property thus
greatly impacted the field of compression. The quality parameters peak signal to noise ratio
(PSNR) and mean square error (MSE) have been calculated. The simulation results shows
that the proposed work improves the PSNR from existing work.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
VIDEO QUALITY ASSESSMENT USING LAPLACIAN MODELING OF MOTION VECTOR DISTRIBUTI...sipij
Video/Image quality assessment (VQA/IQA) is fundamental in various fields of video/image processing.
VQA reflects the quality of a video as most people commonly perceive. This paper proposes a reducedreference
mobile VQA, in which one-dimensional (1-D) motion vector (MV) distributions are used as
features of videos. This paper focuses on reduction of data size using Laplacian modeling of MV
distributions because network resource is restricted in the case of mobile video. The proposed method is
more efficient than the conventional methods in view of the computation time, because the proposed quality
metric decodes MVs directly from video stream in the parsing process rather than reconstructing the
distorted video at a receiver. Moreover, in view of data size, the proposed method is efficient because a
sender transmits only 28 parameters. We adopt the Laplacian distribution for modeling 1-D MV
histograms. 1-D MV histograms accumulated over the whole video sequences are used, which is different
from the conventional methods that assess each image frame independently. For testing the similarity
between MV histogram of reference and distorted videos and for minimizing the fitting error in Laplacian
modeling process, we use the chi-square method. To show the effectiveness of our proposed method, we
compare the proposed method with the conventional methods with coded video clips, which are coded
under varying bit rate, image size, and frame rate by H.263 and H.264/AVC. Experimental results show
that the proposed method gives the performance comparable with the conventional methods, especially, the
proposed method requires much lower transmission data.
An Efficient Block Matching Algorithm Using Logical ImageIJERA Editor
Motion estimation, which has been widely used in various image sequence coding schemes, plays a key role in the transmission and storage of video signals at reduced bit rates. There are two classes of motion estimation methods, Block matching algorithms (BMA) and Pel-recursive algorithms (PRA). Due to its implementation simplicity, block matching algorithms have been widely adopted by various video coding standards such as CCITT H.261, ITU-T H.263, and MPEG. In BMA, the current image frame is partitioned into fixed-size rectangular blocks. The motion vector for each block is estimated by finding the best matching block of pixels within the search window in the previous frame according to matching criteria. The goal of this work is to find a fast method for motion estimation and motion segmentation using proposed model. Recent day Communication between ends is facilitated by the development in the area of wired and wireless networks. And it is a challenge to transmit large data file over limited bandwidth channel. Block matching algorithms are very useful in achieving the efficient and acceptable compression. Block matching algorithm defines the total computation cost and effective bit budget. To efficiently obtain motion estimation different approaches can be followed but above constraints should be kept in mind. This paper presents a novel method using three step and diamond algorithms with modified search pattern based on logical image for the block based motion estimation. It has been found that, the improved PSNR value obtained from proposed algorithm shows a better computation time (faster) as compared to original Three step Search (3SS/TSS ) method .The experimental results based on the number of video sequences were presented to demonstrate the advantages of proposed motion estimation technique.
Shot Boundary Detection In Videos Sequences Using Motion ActivitiesCSCJournals
Video segmentation is fundamental to a number of applications related to video retrieval and analysis. To realize the content based video retrieval, the video information should be organized to elaborate the structure of the video. The segmentation video into shot is an important step to make. This paper presents a new method of shot boundaries detection based on motion activities in video sequence. The proposed algorithm is tested on the various video types and the experimental results show that our algorithm is effective and reliably detects shot boundaries.
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.
Super-resolution (SR) is the process of obtaining a high resolution (HR) image or
a sequence of HR images from a set of low resolution (LR) observations. The block
matching algorithms used for motion estimation to obtain motion vectors between the
frames in Super-resolution. The implementation and comparison of two different types of
block matching algorithms viz. Exhaustive Search (ES) and Spiral Search (SS) are
discussed. Advantages of each algorithm are given in terms of motion estimation
computational complexity and Peak Signal to Noise Ratio (PSNR). The Spiral Search
algorithm achieves PSNR close to that of Exhaustive Search at less computation time than
that of Exhaustive Search. The algorithms that are evaluated in this paper are widely used
in video super-resolution and also have been used in implementing various video standards
like H.263, MPEG4, H.264.
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 VIDEO COMPRESSION TECHNIQUE UTILIZING SPATIO-TEMPORAL LOWER COEFFICIENTSIAEME Publication
With the advancement of communication in recent trends, video compression plays an important role in the transmission of information on social networking and for storage with limited memory capacity. Also the inadequate bandwidth for transmission and lower quality make video compression a serious phenomenon to consider in the field of communication. There is a need to improve the video compression process which can encode the video data with low computational complexity with better quality along with maintaining speed. In this work, a new technique is developed based on the block processing utilizing the lower coefficients between frames.
Recognition and tracking moving objects using moving camera in complex scenesIJCSEA Journal
In this paper, we propose a method for effectively tracking moving objects in videos captured using a
moving camera in complex scenes. The video sequences may contain highly dynamic backgrounds and
illumination changes. Four main steps are involved in the proposed method. First, the video is stabilized
using affine transformation. Second, intelligent selection of frames is performed in order to extract only
those frames that have a considerable change in content. This step reduces complexity and computational
time. Third, the moving object is tracked using Kalman filter and Gaussian mixture model. Finally object
recognition using Bag of features is performed in order to recognize the moving objects.
Fast Motion Estimation for Quad-Tree Based Video Coder Using Normalized Cross...CSCJournals
Motion estimation is the most challenging and time consuming stage in block based video codec. To reduce the computation time, many fast motion estimation algorithms were proposed and implemented. This paper proposes a quad-tree based Normalized Cross Correlation (NCC) measure for obtaining estimates of inter-frame motion. The measure operates in frequency domain using FFT algorithm as the similarity measure with an exhaustive full search in region of interest. NCC is a more suitable similarity measure than Sum of Absolute Difference (SAD) for reducing the temporal redundancy in video compression since we can attain flatter residual after motion compensation. The degrees of homogeneous and stationery regions are determined by selecting suitable initial fixed threshold for block partitioning. An experimental result of the proposed method shows that actual numbers of motion vectors are significantly less compared to existing methods with marginal effect on the quality of reconstructed frame. It also gives higher speed up ratio for both fixed block and quad-tree based motion estimation methods.
VISUAL ATTENTION BASED KEYFRAMES EXTRACTION AND VIDEO SUMMARIZATIONcscpconf
Recent developments in digital video and drastic increase of internet use have increased the
amount of people searching and watching videos online. In order to make the search of the
videos easy, Summary of the video may be provided along with each video. The video summary
provided thus should be effective so that the user would come to know the content of the video
without having to watch it fully. The summary produced should consists of the key frames that
effectively express the content and context of the video. This work suggests a method to extract
key frames which express most of the information in the video. This is achieved by quantifying
Visual attention each frame commands. Visual attention of each frame is quantified using a
descriptor called Attention quantifier. This quantification of visual attention is based on the
human attention mechanism that indicates color conspicuousness and the motion involved seek
more attention. So based on the color conspicuousness and the motion involved each frame is
given a Attention parameter. Based on the attention quantifier value the key frames are extracted and are summarized adaptively. This framework suggests a method to produces meaningful video summary.
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.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Similar to Motion detection in compressed video using macroblock classification (20)
Advanced Computing: An International Journal (ACIJ) is a peer-reviewed, open access peer-reviewed journal that publishes articles which contribute new results in all areas of the advanced computing. The journal focuses on all technical and practical aspects of high performance computing, green computing, pervasive computing, cloud computing etc. The goal of this journal is to bring together researchers and a practitioners from academia and industry to focus on understanding advances in computing and establishing new collaborations in these areas.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of computing.
Call for Papers - Advanced Computing An International Journal (ACIJ) (2).pdfacijjournal
Submit your Research Papers!!!
Advanced Computing: An International Journal ( ACIJ )
ISSN: 2229 -6727 [Online] ; 2229 - 726X [Print]
Webpage URL: http://airccse.org/journal/acij/acij.html
Submission URL: http://coneco2009.com/submissions/imagination/home.html
Submission Deadline : April 08, 2023
Here's where you can reach us : acijjournal@yahoo.com or acij@aircconline
Advanced Computing: An International Journal (ACIJ
)
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Motion detection in compressed video using macroblock classification
1. Advanced Computing: An International Journal (ACIJ), Vol.5, No.2/3, May 2014
DOI : 10.5121/acij.2014.5301 1
MOTION DETECTION IN COMPRESSED VIDEO
USING MACROBLOCK CLASSIFICATION
M.Usha
Department of ECE, MKCE, Karur, Tamil Nadu
ABSTRACT
In this paper, to detect the moving objects between frames in compressed video and to obtain the best
compression video and the noiseless video. We describe a video in which frames by classifying
macroblocks (MB), and describe motion estimation (ME), motion vector field (MV) and motion
compensation (MC). we propose to classify Macroblocks of each video frame into different classes and use
this class information to describe the frame content based on the motion vector. MB class information
video applications such as shot change detection, motion discontinuity detection, Outlier rejection for
global motion estimation. To reduce the noise and to improve the clarity of the compressed video by using
contrast limited adaptive histogram equalization (CLAHE) Algorithm.
KEYWORDS
Motion estimation, Macro block Classification, Motion vector, Motion Compensation, CLAHE.
I. INTRODUCTION
Video is a prominent multimedia data form in today‘s communication systems. Hence it‘s
processing and analysis is of vital consequence. Video processing techniques such as video
compression, video content analysis, compensation, extraction, etc are important in many
applications. Motion based features play an important role in video signal processing since they
manipulate the real-time, ―dynamicǁ parameters of video signal.
Most professional projects have an offline phase that uses compressed video and then an online,
finishing phase that uses uncompressed video recaptured at full resolution. Uncompressed video
requires expensive VTRs and large, high-speed hard disks.
Noise is a very important factor for image quality. Noise is a random variation of image density,
visible as grain in film and pixel level variations in digital images. It arises from the effects of
basic physics the photon nature of light and the thermal energy of heat inside image sensors.
Typical noise reduction (NR) software reduces the visibility of noise.
The video compression is achieved by identifying high intensity moving object in compressed
video frames. To estimate the motion by using macroblock classification method and based on
motion vector field by using three classes. Three applications such as shot change detection,
motion discontinuity detection and by using contrast limited adaptive histogram equalization
(CLAHE) Algorithm to reduce the noise and to improve the clarity of the compressed video.
The rest of the paper organized as follows: video compression overview described,
Macroblock(MB) classification method described, Compression method and proposes two
2. Advanced Computing: An International Journal (ACIJ), Vol.5, No.2/3, May 2014
2
applications such as shot change detection, motion discontinuity detection.Last Section describes
CLAHE algorithm.
2. OVERVIEW
To estimate the motion by using macroblock classification method and based on motion vector
field by using three classes. There are many applications such as shot change detection, motion
discontinuity detection and outlier rejection using global estimation. Thus shot change detection,
one of the applications was chosen and proved using macroblock classification method and by
comparing threshold values and PSNR values.
By using contrast limited adaptive histogram equalization (CLAHE) Algorithm to reduce the
noise and to improve the clarity of the compressed video. Thus the compressed video was further
compressed, it will less in memory space and good in clarity while displaying.
3. VIDEO COMPRESSION
Film frame or video frame is one of the many still (or nearly so) images which compose the
complete moving picture. Since the Video data may occupy more bandwidth than the other media
data during transmission, it should be given more emphasis in the wireless multimedia
communication. Once a video signal is digital, it requires a large amount of storage space and
transmission bandwidth.
Sources of redundancy:
Temporal – Adjacent frames highly correlated.
Spatial – Nearby pixels are often correlated with each other.
To reduce the amount of data, several strategies are employed that compress the information
without negatively affecting the quality of the image.FIG.1 means input video is converted to
number of frames and thus frames undergoes macroblock classification and motion estimation
motion vector field for the prediction and the intensity of motion pixels. Thus compensation
down for compressed video frames and using of CLAHE algorithm enhanced clarity compressed
video can be obtained without any error.
Basically video process having four important processes follows:
Frame conversion
Motion Estimation
Motion vector field calculation
Motion compensation
FRAMES CONVERSION: First of all, compressed video is going to convert number of frames.
Because process depends on frames only
MOTION ESTIMATION: Motion estimation explores the temporal redundancy, which is
inherent in video sequences, and it represents a basis for lossy video compression. Motion
estimation uses the comparison of the adjacent frames. It is an important process in which
comparison between frames.
3. Advanced Computing: An International Journal (ACIJ), Vol.5, No.2/3, May 2014
3
MOTION VECTOR FIELD: The displacement of the reference macroblock to the target
macroblock is called a motion vector MV.
MOTION COMPENSATION: It is an algorithm technique employed in the encoding of video
data for video compression.
Figure 1. Block Diagram
4. MOTION ESTIMATION
Motion estimation used as the basis for powerful video analysis and video processing. Motion
estimation explores the temporal redundancy, which is inherent in video sequences, and it
represents a basis for lossy video compression. Motion estimation is often performed in the
macroblock domain. It identifies same pixel position in the reference frame by comparing the
current frame. Estimation in video compression efficiency of the system is mainly reflected in
image quality, compression rate and search speed. The basic principle is the use of adjacent
frames in video sequences, the temporal correlation and spatial correlation. Establish the
relationship between the sequence adjacent to the inter-frame expression, thereby reducing the
temporal redundancy and spatial redundancy; improve the efficiency of video coding.
In a video solution, the motion estimation computation is generally 60-80% of the total
computation; the results directly affect the quality of the video image coding efficiency and
recovery. Therefore, efficient motion estimation algorithm has a very important significance to
improve the video data compression coding efficiency. Improve image quality, speed up the
estimated speed and reduce the bit rate is the goal of motion estimation algorithm.
Motion estimation is that one block b of a current frame C is sought for in a previous frame If a
block of pixels which is similar enough to block b is found in R , then instead of transmitting the
whole block just a ―motion vectorǁ is calculated. After finding out motion estimation and motion
vector field motion compensation is achieved.
4. Advanced Computing: An International Journal (ACIJ), Vol.5, No.2/3, May 2014
4
The name motion picture comes from the fact that a video, once encoded, is nothing but a
sequence of still pictures that are shown at a reasonably high frequency. That gives the viewer the
illusion that it is in fact a continuous animation. Each frame is shown for one small fraction of a
second, more precisely 1/ k seconds, where k is the number of frames per second.
Coming back to the definition of a scene, where the frames are captured without interruption, one
can expect consecutive frames to be quite similar to one another, as very little time is allowed
until the next frame is to be captured.
5. MOTION VECTOR FIELD
In video compression, a motion vector is the key element in the motion estimation process. It is
used to represent a macroblock in a picture based on the position of this macroblock (or a similar
one) in another picture, called the reference picture. A two-dimensional vector used for inter
prediction that provides an offset from the coordinates in the decoded picture to the coordinates in
a reference picture.
All macroblocks in a video frame are processed in raster scan order in the space domain, so the
adjacent macro blocks in the upper left, upper right and the left up can be well used as a reference
macroblock. Use this algorithm to support regional prediction MV of the target macroblock D is
decided by A, B, C, three macro blocks of the MV. A, B, C, macroblock MV in one to predict the
MV of the target macroblock D and to get the D macro block MV predictive value using the three
classes.
Full advantage of the spatial and temporal correlation of video sequences, the use of the adjacent
macroblock motion vector to block movement by type starting point for prediction using different
search strategies on the macro block. The results show that, in the case of the image quality is
slightly improved, compared with original MVFAST algorithm, the improved algorithm can
effectively improve the encoding speed.
6. MB CLASSIFICATION METHOD
Image compression component and technique based on wavelet transform used on still image and
video frames. MB is usually composed of two or more blocks of pixels. Size of the block is
usually a multiple of 4. Typically, pictures (frames) are segmented into macroblocks and
individual prediction types can be selected on a macroblock basis rather than being the same for
the entire picture.
Each image frame is divided into a fixed number of usually square blocks. For each block in the
frame, a search is made in the reference frame over an area of the image that allows for the
maximum translation that the coder can use. The search is for the best matching block, to give the
least prediction error, usually minimizing either mean square difference, or mean absolute
difference which is easier to compute. Typical block sizes are of the order of 16x16 pixels, and
the maximum displacement might be +-64 pixels from a block's original position. Several search
strategies are possible, usually using some kind of sampling mechanism, but the most
straightforward approach is exhaustive search.
5. Advanced Computing: An International Journal (ACIJ), Vol.5, No.2/3, May 2014
5
Figure 2. Macroblocks in a frame
Figure 3.Comparison between frames
Motion estimation used as the basis for powerful video analysis and video processing. The
macroblock motion decision was proposed to reduce the computational complexity of the motion
estimation process in compressed video [3].Low-complexity encoding can be realized by limiting
the amount of motion estimation performed at the encoder [4]. It identifies same pixel position in
the reference frame by comparing the current frame.
6. Advanced Computing: An International Journal (ACIJ), Vol.5, No.2/3, May 2014
6
For each reference frame, the motion estimation algorithm is executed for all possible modes of
an MB[3]. Low-complexity encoding can be realized by limiting the amount of motion estimation
performed at the encoder[4]. FIG.3 Motion estimation is that one block b of a current frame C is
sought for in a previous frame R. If a block of pixels which is similar enough to block b is found
in R, then instead of transmitting the whole block just a ―motion vectorǁ is calculated. After
finding out motion estimation and motion vector field motion compensation is achieved.
Figure 4.Macroblock Classification
Sequence statistics are used to predict macroblock type prior to coding, enabling selective
computation of functions such as motion estimation (ME), motion vector field (MV) and motion
compensation (MC) [2]. Each slice consists of macroblocks, which are blocks of 16x16.
However, each macroblock is also divided into sub macroblock partitions for motion-
compensated prediction[1].Without loss of generality, the MB classification method can be
described as,
The motivations of classifying MBs according to three classes can be summarized as follows:
1)According to three classes, MBs in Class 1 have two features: (a) their MVs can be predicted
accurately (i.e., is calculated based on the motion information of spatial or temporal neighboring
7. Advanced Computing: An International Journal (ACIJ), Vol.5, No.2/3, May 2014
7
MBs). This means that the motion patterns of these MBs are regular (i.e., can be predicted) and
smooth (i.e., coherent with the previous-frame motions).
(b) They have small matching cost values. This means that these MBs can find good matches
from the previous frames. Therefore, the Class 1 information can be viewed as an indicator of the
content correlation between frames
2) Class 2 includes MBs whose motion cannot be accurately predicted by their neighboring
information and their previous motion information. This means that the motion patterns of these
MBs are irregular and unsmooth from those of the previous frames. Therefore, the Class 2
information can be viewed as an indicator of the motion un smoothness between frames.
3) Class 3 includes MBs whose are close to the and whose matching cost values are large.
Therefore, Class 3 MBs will include areas with complex textures but similar motion patterns to
the previous frames.
Since is only available in the ME process, (1) is more suitable for applications where video
coding and other video processing are performed at the same time, such as global motion
estimation, rate control, computation control coding, as well as labeling shot changes in the
process of compressing videos. However, it should be noted that (1) is only an implementation
example of the proposed classification method. The idea of the proposed MB classification is
general and it can be easily extended to other forms for different applications like shot change
detection.
CLASS 1: differentiate the pixels in grey color
CLASS 2: differentiate the pixels in black color
CLASS 3: differentiate the pixels in white color
Figure 5.Class 2 Figure 6.Class 3
6. MOTION COMPENSATION
It is an algorithm technique employed in the encoding of video data for video compression. When
images can be accurately synthesized from previously transmitted/stored images, the compression
efficiency can be improved. The blocks are not transformed in any way apart from being shifted
to the position of the predicted block. This shift is represented by a motion vector. By using the
motion estimation and motion vector field , motion compensation can be achieved.
8. Advanced Computing: An International Journal (ACIJ), Vol.5, No.2/3, May 2014
8
The choice of block-size to use for motion compensation is always a compromise, smaller and
more numerous blocks can better represent complex motion than fewer large ones. This reduces
the work and transmission costs of subsequent correction stages but with greater cost for the
motion information itself. And they conclude that the choice of block-size can be affected not
only by motion vector accuracy but also by other scene characteristics such as texture and inter-
frame noise.
7. RESULTS
Figure 7. Original Video
Figure7.Reference Frame
9. Advanced Computing: An International Journal (ACIJ), Vol.5, No.2/3, May 2014
9
Figure 8.Current Frame
Figure 9.Motion Compensation
10. Advanced Computing: An International Journal (ACIJ), Vol.5, No.2/3, May 2014
10
Figure 10.CLAHE Output
8. APPLICATIONS
8.1. SHOT CHANGE DETECTION
A crucial step in multimedia processing is that of reliable video segmentation into visually
coherent video shots (i.e., scene change detection). FIG. 9 Frames captured by one camera action
(i.e., a continuous operation of one camera), and a „shot change‟ as the boundary of two shots.
Video shots,as tracking of selected video information [5].
Therefore, we can use the information of Class 1 as the primary feature to detect shot changes.
Since the motion pattern will also change at shot changes, the information of Class 2 and Class 3
can be used as additional features for shot change detection. However, they can be easily
extended for use with different block-based transform video compression methods[5].
FIG.10(a)&(b) transitions from one shot to another, either abrupt or gradual, may take place. An
abrupt transition, or hard cut, occurs between twoconsecutive frames and is the most common
type.[6]
There are two types of shot changes .They are:
Hard cut(abrupt change)
Fades(gradual change)
8.1.1 Hard cut(Abrupt change):
A cut is an instantaneous transition from one scene to the next. There are no transitional frames
between two shots.
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Figure 11. Example for Hard Cut
8.1.2 Fades (gradual change):
A fade is a gradual transition between a scene and a constant image (fade-out) or between a
constant image and a scene (fade-in).
Figure 12. Example for Fades Out
Since, shot changes (including abrupt, gradual, fade-in or fade-out) always happen between two
uncorrelated video shots, the content correlation between frames at shot changes will be low.
Therefore we can use the information of class 1 as the primary feature to detect shot changes.
Furthermore , since the motion pattern will also changes, the information of class 2 and class 3
can be used as additional shot change detection. Thus according to the three classes the class
based algorithm (CB-Shot) for shot change detection is proposed as below equation (a)
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From the equation(a) the class based algorithm ,where ‘t‘ is the frame number and Fgshot(t) is a
flag indicating wheather a shot change happens at the current frame t or not. Fgshot(t) will equal
to 1 if there is a shot change and will equal to 0 else.
CLASS 1 Indicate low content correlation
CLASS 2
CLASS 3 Detect rapid motion changes in frame.
NIntra_MB(t) is the number of intra coded macroblocks at the frame ‘t‘ . NIR(t) is the number of
intra-refresh macroblocks in the current frame.
Nclass_1(t) , Nclass_2(t) and Nclass_3(t) are the total number of class 1 ,class 2and class 3 MBs
in the current frame t respectively.
Fgshot(t) = 1 shot change occurs
Fgshot(t) = 0 no shot change
T1,T2,T3 and T4 are the thresholds for deciding the shot change. T1-T4 are calculated by
equation (b)
T1=(NMB (t)) - (NIR(t))/40
T2=(NMB (t)) - (NIR(t))/30
T3=(NMB (t)) - ( NIR(t))/4,T4=T1
Where NMB (t) is the total number of MBs of all classes in the current frame. It should be noted
that in Fgshot(t) equation the Class 1 information is the main feature for detecting shot changes
(i.e. Nclass_1(t), Nclass_1(t) ≤ T2 in equation(a)). The intuitive of using the Class 1 information
as the major feature is that it is a good indicator of the content correlation between frames. The
Class 2 and Class 3 information is used to help detect frames at the beginning of some gradual
shot changes where a large change in motion pattern has been detected but the number of Class 1
MBs has not yet decreased to a small number.
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The intra-coded MB infor-mation can help discard the possible false alarm shot changes due to
the MB misclassification. From, (a) and (b), we can also see that when intra-refresh functionality
is enabled (i.e., when NIR(t) > 0 ), our algorithm can be extended by simply ex-cluding these
intra-refreshed MBs and only performing shot change detection based on the remaining MBs.
Furthermore, note that (a) is only one implementation of using our class information for shot
change detection. We can easily extend (a) by using more sophisticated methods suchas cross-
validation to decide the threshold values in an automatic way. Besides, other machine learning
models can also be used to decide the shot detection rules and to take the place of the manually-
set rules in (a)
8.2. MOTION DISCONTINUITY DETECTION
Automatic detection of independently moving targets in a potentially large collection of
surveillance video data obtained from an unmanned sensor(such as video camera in an unmanned
aerial vehicle).Consequently this work is about independent motion estimation[7]. Since our class
information, especially Class 2 information, can efficiently reflect the irregular motion patterns, it
can be easily used for motion discontinuity detection.
Figure 13. Example for Motion Discontinuity
Basically, motion discontinuity can be viewed as motion unsmoothness or the change of motion
patterns. Where I(f) is an indicator I will equal to 1 if f is true, and 0 if f is false. FIG.11 means
that an MD will be detected only if the number of Class 2 MBs is larger than a threshold for k+1
consecutive frames. This is based on the assumption that an obvious camera motion change will
affect several frames rather than one.
9. CLAHE ALGORITHM
Contrast limiting procedure has to be applied for each neighbourhood from which a
transformation function is derived. Contrast limited adaptive histogram equalization (CLAHE)
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was developed to prevent the over amplification of noise. FIG.12 A „Claire‟ video sequence was
used to study the effect of the Quality Scaling Factor (QSF) of a standard Codec on the
compression ratio of the proposed encoder.
QSF is the value of the quantizing parameter of a standard encoder .[8] CLAHE was developed to
prevent the over amplification of noise that adaptive histogram equalization a block in a standard
encoder are divided by this QSF for achieving higher compression. This higher the value of QSF,
higher will be the compression achieved. The quality however goes down (because increased
quantization error). Thus QSF provides a mean for trade off between quality and compression. [8]
Figure 14. CLAHE Algorithm
Use the displayed image to estimate the intensity transfer that is then applied to all channels
individually. This is the desired mode for getting local contrast compression for color
photographs with a higher bit-depth and higher dynamic range than appropriate for a digital
display.
Figure 15.Original image &CLAHE processed
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10. CONCLUSION
In this paper, a new Macro Block class information is proposed for various video processing
applications. We have to classify Macro blocks of each frame into different classes and use this
class information to describe the frame content. Thus by using class information many algorithms
can be achieved for many video applications. The CLAHE algorithm is the promising candidate
for the noise reduction and enhancement of video quality of compressed video. However,
CLAHE is still a young–born research field and several issues need to be addressed to fully
understand its potential and limitations in practical.
REFERENCES
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Author
Usha.M received the B.E. degree from Anna University Trichy, Tamil Nadu, in 2011,
the M.E. degree from Anna University Chennai, Tamil Nadu, in 2013,Since 2013, she
has been an Assistant Professor with Department of Electronic and Communication
Engineering. Her research interests include video processing, video coding &
compression.