This document summarizes a research paper that surveyed different video scene change detection techniques. It began with definitions of key terms related to video and scene change detection. It then reviewed several papers published between 1997-2005 that proposed various techniques for detecting scene changes in video sequences. The techniques included using metrics to detect changes between frames, a hierarchical approach using compressed video properties, detecting wipe transitions through image mapping and analysis, and combining audio and video clues. The document discussed limitations and opportunities for future work with each technique.
Review On Different Feature Extraction AlgorithmsIRJET Journal
This document discusses different feature extraction algorithms that can be used in visual sensor networks (WVSN). It begins by explaining the traditional "compress-then-analyze" approach and introduces the newer "analyze-then-compress" approach. The document then reviews several real-valued and binary feature extraction algorithms such as SIFT, SURF, BRIEF, BRISK, and BinBoost. It proposes a video coding system that uses the BRISK algorithm to extract features, encodes the features using entropy coding, and transmits the data to a base station for matching with stored image data. Diagrams of the proposed system and the BRISK algorithm are also included.
The document discusses implementing the Diamond Search algorithm for motion estimation in video compression using parallel processing on a GPU. Motion estimation is the most computationally expensive part of video compression. The Diamond Search algorithm was implemented on an NVIDIA GeForce 610 GPU using CUDA. Experimental results showed a 4x speedup compared to CPU implementation, demonstrating that GPUs can accelerate motion estimation to reduce video encoding time. Implementing fast motion estimation algorithms in parallel on GPUs is an effective approach for real-time video applications.
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
Secured Data Transmission Using Video Steganographic SchemeIJERA Editor
Steganography is the art of hiding information in ways that avert the revealing of hiding messages. Video Steganography is focused on spatial and transform domain. Spatial domain algorithm directly embedded information in the cover image with no visual changes. This kind of algorithms has the advantage in Steganography capacity, but the disadvantage is weak robustness. Transform domain algorithm is embedding the secret information in the transform space. This kind of algorithms has the advantage of good stability, but the disadvantage of small capacity. These kinds of algorithms are vulnerable to steganalysis. This paper proposes a new Compressed Video Steganographic scheme. The data is hidden in the horizontal and the vertical components of the motion vectors. The PSNR value is calculated so that the quality of the video after the data hiding is evaluated.
This document discusses objective video quality measurement based on the human visual system. It introduces various deblocking algorithms used to improve the quality of reconstructed video by reducing blocking artifacts. It also discusses limitations of traditional PSNR metrics and proposes a no-reference quality assessment method. The proposed method considers aspects of the human visual system like masking effects and uses algorithms in the DCT domain and post-processing to evaluate video quality in a way that correlates better with subjective human perception. Experimental results on distorted video sets demonstrate the effectiveness of the proposed no-reference quality measurement approach.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
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.
This document proposes an adaptive rood pattern search (ARPS) algorithm for fast block matching motion estimation. ARPS uses adjustable search patterns centered around a predicted motion vector value to quickly locate the minimum matching error point. It employs a checking bit-map and zero-motion prejudgment to avoid duplicate computations and benefit sequences with small motion. Experimental results show ARPS achieves 94-447x speedup over full search with less than 0.12dB drop in PSNR quality compared to full search for most sequences.
Review On Different Feature Extraction AlgorithmsIRJET Journal
This document discusses different feature extraction algorithms that can be used in visual sensor networks (WVSN). It begins by explaining the traditional "compress-then-analyze" approach and introduces the newer "analyze-then-compress" approach. The document then reviews several real-valued and binary feature extraction algorithms such as SIFT, SURF, BRIEF, BRISK, and BinBoost. It proposes a video coding system that uses the BRISK algorithm to extract features, encodes the features using entropy coding, and transmits the data to a base station for matching with stored image data. Diagrams of the proposed system and the BRISK algorithm are also included.
The document discusses implementing the Diamond Search algorithm for motion estimation in video compression using parallel processing on a GPU. Motion estimation is the most computationally expensive part of video compression. The Diamond Search algorithm was implemented on an NVIDIA GeForce 610 GPU using CUDA. Experimental results showed a 4x speedup compared to CPU implementation, demonstrating that GPUs can accelerate motion estimation to reduce video encoding time. Implementing fast motion estimation algorithms in parallel on GPUs is an effective approach for real-time video applications.
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
Secured Data Transmission Using Video Steganographic SchemeIJERA Editor
Steganography is the art of hiding information in ways that avert the revealing of hiding messages. Video Steganography is focused on spatial and transform domain. Spatial domain algorithm directly embedded information in the cover image with no visual changes. This kind of algorithms has the advantage in Steganography capacity, but the disadvantage is weak robustness. Transform domain algorithm is embedding the secret information in the transform space. This kind of algorithms has the advantage of good stability, but the disadvantage of small capacity. These kinds of algorithms are vulnerable to steganalysis. This paper proposes a new Compressed Video Steganographic scheme. The data is hidden in the horizontal and the vertical components of the motion vectors. The PSNR value is calculated so that the quality of the video after the data hiding is evaluated.
This document discusses objective video quality measurement based on the human visual system. It introduces various deblocking algorithms used to improve the quality of reconstructed video by reducing blocking artifacts. It also discusses limitations of traditional PSNR metrics and proposes a no-reference quality assessment method. The proposed method considers aspects of the human visual system like masking effects and uses algorithms in the DCT domain and post-processing to evaluate video quality in a way that correlates better with subjective human perception. Experimental results on distorted video sets demonstrate the effectiveness of the proposed no-reference quality measurement approach.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
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.
This document proposes an adaptive rood pattern search (ARPS) algorithm for fast block matching motion estimation. ARPS uses adjustable search patterns centered around a predicted motion vector value to quickly locate the minimum matching error point. It employs a checking bit-map and zero-motion prejudgment to avoid duplicate computations and benefit sequences with small motion. Experimental results show ARPS achieves 94-447x speedup over full search with less than 0.12dB drop in PSNR quality compared to full search for most sequences.
IRJET- Study of SVM and CNN in Semantic Concept DetectionIRJET Journal
1) The document discusses approaches for semantic concept detection in videos using techniques like support vector machines (SVM) and convolutional neural networks (CNN).
2) It proposes a concept detection system that uses SVM and CNN together, extracting features from key frames using Hue moments and classifying the features with SVM and CNN.
3) The outputs of SVM and CNN are fused to improve concept detection accuracy compared to using the classifiers individually. Fusing the two classifiers is intended to better identify the concepts in video frames.
This document compares different block-matching motion estimation algorithms. It introduces block-matching motion estimation and describes popular distortion metrics like MSE and SAD. It then explains the full-search algorithm and more efficient algorithms like three-step search and four-step search that evaluate fewer candidate blocks to reduce computational cost. These algorithms are evaluated and compared using video test sequences to analyze their performance and quality.
Robust Image Watermarking Scheme Based on Wavelet TechniqueCSCJournals
In this paper, an image watermarking scheme based on multi bands wavelet transformation method is proposed. At first, the proposed scheme is tested on the spatial domain (for both a non and semi blind techniques) in order to compare its results with a frequency domain. In the frequency domain, an adaptive scheme is designed and implemented based on the bands selection criteria to embed the watermark. These criteria depend on the number of wavelet passes. In this work three methods are developed to embed the watermark (one band (LL|HH|HL|LH), two bands (LL&HH | LL&HL | LL&LH | HL&LH | HL&HH | LH&HH) and three bands (LL&HL&LH | LL&HH&HL | LL&HH&LH | LH&HH&HL) selection. The analysis results indicate that the performance of the proposed watermarking scheme for the non-blind scheme is much better than semi-blind scheme in terms of similarity of extracted watermark, while the security of semi-blind is relatively high. The results show that in frequency domain when the watermark is added to the two bands (HL and LH) for No. of pass =3 led to good correlation between original and extracted watermark around (similarity = 99%), and leads to reconstructed images of good objective quality (PSNR=24 dB) after JPEG compression attack (QF=25). The disadvantage of the scheme is the involvement of a large number of wavelet bands in the embedding process.
This document summarizes a project that used a deep learning model to predict depth images from single RGB images. It discusses existing solutions using stereo cameras or Kinect devices. The project used the NYU Depth V2 dataset, splitting it into training, validation, and test sets. It implemented a model based on previous work, training it on RGB-D image pairs for 35 epochs but achieving only moderate results due to limited training data. The code and results are available online for further exploration.
This document summarizes research on using indexing techniques for efficient image retrieval. It discusses using content-based image retrieval (CBIR) to extract image features and store them for efficient comparison to query images. CBIR techniques described include color layout, edge histogram, scalable color, and relevance feedback to iteratively collect user feedback and improve retrieval performance over multiple cycles. The document also examines using various indexing and querying methods like semantic searching of image graphs to enhance image retrieval efficiency.
This document presents a technique for data hiding using double key integer wavelet transform. The cover image is first transformed to the integer wavelet domain. Then coefficients are randomly selected using Key 2 for embedding secret data. Key 1 determines the number of bits embedded in each coefficient. The optimal pixel adjustment process is applied to improve quality. This technique aims to achieve high hiding capacity, security and visual quality. Experimental results on test images show the proposed method embedding more data than other techniques while maintaining good peak signal to noise ratios, especially when using certain key combinations.
Comparison of different Fingerprint Compression Techniquessipij
The important features of wavelet transform and different methods in compression of fingerprint images have been implemented. Image quality is measured objectively using peak signal to noise ratio (PSNR) and mean square error (MSE).A comparative study using discrete cosine transform based Joint Photographic Experts Group(JPEG) standard , wavelet based basic Set Partitioning in Hierarchical trees(SPIHT) and Modified SPIHT is done. The comparison shows that Modified SPIHT offers better compression than basic SPIHT and JPEG. The results will help application developers to choose a good wavelet compression system for their applications.
Fast Full Search for Block Matching Algorithmsijsrd.com
This project introduces configurable motion estimation architecture for a wide range of fast block-matching algorithms (BMAs). Contemporary motion estimation architectures are either too rigid for multiple BMAs or the flexibility in them is implemented at the cost of reduced performance. In block-based motion estimation, a block-matching algorithm (BMA) searches for the best matching block for the current macro block from the reference frame. During the searching procedure, the checking point yielding the minimum block distortion (MBD) determines the displacement of the best matching block.
Smartphone indoor positioning based on enhanced BLE beacon multi-laterationTELKOMNIKA JOURNAL
In this paper, we introduce a smartphone indoor positioning method using bluetooth low energy (BLE) beacon multilateration. At first, based on signal strength analysis, we construct a distance calculation model for BLE beacons. Then, with the aims to improve positioning accuracy, we propose an improved lateral method (range-based method) which is applied for 4 nearby beacons. The method is intended to design a real-time system for some services such as emergency assistance, personal localization and tracking, location-based advertising and marketing, etc. Experimental results show that the proposed method achieves high accuracy when compared with the state of the art lateral methods such as geometry-based (conventional trilateration), least square estimation-based (LSE-based) and weighted LSE-based.
This document proposes a new shape-adaptive reversible integer lapped transform (SA-RLT) method for region-of-interest (ROI) coding of 2D remote sensing images. SA-RLT performs better than other transforms like SA-DWT and SA-DCT. The method segments the ROI using a new algorithm rather than hand segmentation. It then designs a SA-RLT based ROI compression scheme using object-based set partitioned embedded block coding (OBSPECK). Experimental results show that SA-RLT compression outperforms DCT and DWT compression for remote sensing images. The method provides flexible bit rate control and allows lossless ROI coding without background areas.
SVM Based Saliency Map Technique for Reducing Time Complexity in HEVCIRJET Journal
This document proposes a technique using support vector machine-based saliency maps to reduce computational complexity in HEVC video coding. It describes saliency detection methods that identify prominent regions of images and discusses implementing an SVM-based saliency detection algorithm in MATLAB. The results show transmitting time is significantly reduced compared to full search, reducing complexity while maintaining comparable compression efficiency.
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.
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.
Survey on Various Image Denoising TechniquesIRJET Journal
This document summarizes several techniques for image denoising. It begins by defining image noise and explaining how noise degrades image quality. It then reviews 7 different published techniques for image denoising, summarizing the key aspects of each technique. These include methods using local spectral component decomposition, SVD-based denoising, patch-based near-optimal denoising, LPG-PCA denoising, trivariate shrinkage filtering, SURE-LET denoising, and 3D transform-domain collaborative filtering. The document concludes that LSCD provides better denoising results according to PSNR analysis and provides an overview of the state-of-the-art in image denoising techniques.
High Speed Data Exchange Algorithm in Telemedicine with Wavelet based on 4D M...Dr. Amarjeet Singh
Existing Medical imaging techniques such as fMRI, positron emission tomography (PET), dynamic 3D ultrasound and dynamic computerized tomography yield large amounts of four-dimensional sets. 4D medical data sets are the series of volumetric images netted in time, large in size and demand a great of assets for storage and transmission. Here, in this paper, we present a method wherein 3D image is taken and Discrete Wavelet Transform(DWT) and Dual-Tree Complex Wavelet Transform(DTCWT) techniques are applied separately on it and the image is split into sub-bands. The encoding and decoding are done using 3D-SPIHT, at different bit per pixels(bpp). The reconstructed image is synthesized using Inverse DWT technique. The quality of the compressed image has been evaluated using some factors such as Mean Square Error(MSE) and Peak-Signal to Noise Ratio (PSNR).
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.
A Novel Blind SR Method to Improve the Spatial Resolution of Real Life Video ...IRJET Journal
This document proposes a novel blind super resolution method to improve the spatial resolution of real-life video sequences. The key aspects of the proposed method are:
1) It estimates blur without knowing the point spread function or noise statistics using a non-uniform interpolation super resolution method and multi-scale processing.
2) It uses a cost function with fidelity and regularization terms of a Huber-Markov random field to preserve edges and fine details in the reconstructed high resolution frames.
3) It performs masking to suppress artifacts from inaccurate motions, adaptively weighting the fidelity term at each iteration for faster convergence.
The method is tested on real-life videos with complex motions, objects, and brightness changes, showing
Leading water utility company in USA was facing a challenge to improve pipeline inspection process to reduce human errors and manual inspection time.Pipeline Anomaly Detection automates the process of identification of defects in pipeline videos, by a camera which notes the observations and lastly it generates the report.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
The document discusses video compression using the Set Partitioning in Hierarchical Trees (SPIHT) algorithm and neural networks. It presents the principles of SPIHT coding and the backpropagation algorithm for neural networks. Various neural network training algorithms are tested for compressing video frames, including gradient descent with momentum and adaptive learning. The results show the compressed frames with different algorithms, and gradient descent with momentum and adaptive learning achieved the best compression ratio of 1.1737089:1 while maintaining image clarity.
Video Content Identification using Video Signature: SurveyIRJET Journal
This document summarizes previous research on video content identification using video signatures. It discusses three types of video signatures (spatial, temporal, and spatio-temporal) that have been used to generate unique descriptors to identify identical video scenes. The document then reviews several existing methods for video signature extraction and matching, including techniques based on ordinal signatures, motion signatures, color histograms, local descriptors using interest points, and compressed video shot matching using dominant color profiles. It concludes by proposing a new temporal signature-based method that aims to accurately detect a video segment embedded in a longer unrelated video by extracting frame-level features, generating fine and coarse signatures, and performing frame-by-frame signature matching.
An unsupervised method for real time video shot segmentationcsandit
Segmentation of a video into its constituent shots
is a fundamental task for indexing and
analysis in content based video retrieval systems.
In this paper, a novel approach is presented
for accurately detecting the shot boundaries in rea
l time video streams, without any a priori
knowledge about the content or type of the video. T
he edges of objects in a video frame are
detected using a spatio-temporal fuzzy hostility in
dex. These edges are treated as features of the
frame. The correlation between the features is comp
uted for successive incoming frames of the
video. The mean and standard deviation of the corre
lation values obtained are updated as new
video frames are streamed in. This is done to dynam
ically set the threshold value using the
three-sigma rule for detecting the shot boundary (a
brupt transition). A look back mechanism
forms an important part of the proposed algorithm t
o detect any missed hard cuts, especially
during the start of the video. The proposed method
is shown to be applicable for online video
analysis and summarization systems. In an experimen
tal evaluation on a heterogeneous test set,
consisting of videos from sports, movie songs and m
usic albums, the proposed method achieves
99.24% recall and 99.35% precision on the average
IRJET- Study of SVM and CNN in Semantic Concept DetectionIRJET Journal
1) The document discusses approaches for semantic concept detection in videos using techniques like support vector machines (SVM) and convolutional neural networks (CNN).
2) It proposes a concept detection system that uses SVM and CNN together, extracting features from key frames using Hue moments and classifying the features with SVM and CNN.
3) The outputs of SVM and CNN are fused to improve concept detection accuracy compared to using the classifiers individually. Fusing the two classifiers is intended to better identify the concepts in video frames.
This document compares different block-matching motion estimation algorithms. It introduces block-matching motion estimation and describes popular distortion metrics like MSE and SAD. It then explains the full-search algorithm and more efficient algorithms like three-step search and four-step search that evaluate fewer candidate blocks to reduce computational cost. These algorithms are evaluated and compared using video test sequences to analyze their performance and quality.
Robust Image Watermarking Scheme Based on Wavelet TechniqueCSCJournals
In this paper, an image watermarking scheme based on multi bands wavelet transformation method is proposed. At first, the proposed scheme is tested on the spatial domain (for both a non and semi blind techniques) in order to compare its results with a frequency domain. In the frequency domain, an adaptive scheme is designed and implemented based on the bands selection criteria to embed the watermark. These criteria depend on the number of wavelet passes. In this work three methods are developed to embed the watermark (one band (LL|HH|HL|LH), two bands (LL&HH | LL&HL | LL&LH | HL&LH | HL&HH | LH&HH) and three bands (LL&HL&LH | LL&HH&HL | LL&HH&LH | LH&HH&HL) selection. The analysis results indicate that the performance of the proposed watermarking scheme for the non-blind scheme is much better than semi-blind scheme in terms of similarity of extracted watermark, while the security of semi-blind is relatively high. The results show that in frequency domain when the watermark is added to the two bands (HL and LH) for No. of pass =3 led to good correlation between original and extracted watermark around (similarity = 99%), and leads to reconstructed images of good objective quality (PSNR=24 dB) after JPEG compression attack (QF=25). The disadvantage of the scheme is the involvement of a large number of wavelet bands in the embedding process.
This document summarizes a project that used a deep learning model to predict depth images from single RGB images. It discusses existing solutions using stereo cameras or Kinect devices. The project used the NYU Depth V2 dataset, splitting it into training, validation, and test sets. It implemented a model based on previous work, training it on RGB-D image pairs for 35 epochs but achieving only moderate results due to limited training data. The code and results are available online for further exploration.
This document summarizes research on using indexing techniques for efficient image retrieval. It discusses using content-based image retrieval (CBIR) to extract image features and store them for efficient comparison to query images. CBIR techniques described include color layout, edge histogram, scalable color, and relevance feedback to iteratively collect user feedback and improve retrieval performance over multiple cycles. The document also examines using various indexing and querying methods like semantic searching of image graphs to enhance image retrieval efficiency.
This document presents a technique for data hiding using double key integer wavelet transform. The cover image is first transformed to the integer wavelet domain. Then coefficients are randomly selected using Key 2 for embedding secret data. Key 1 determines the number of bits embedded in each coefficient. The optimal pixel adjustment process is applied to improve quality. This technique aims to achieve high hiding capacity, security and visual quality. Experimental results on test images show the proposed method embedding more data than other techniques while maintaining good peak signal to noise ratios, especially when using certain key combinations.
Comparison of different Fingerprint Compression Techniquessipij
The important features of wavelet transform and different methods in compression of fingerprint images have been implemented. Image quality is measured objectively using peak signal to noise ratio (PSNR) and mean square error (MSE).A comparative study using discrete cosine transform based Joint Photographic Experts Group(JPEG) standard , wavelet based basic Set Partitioning in Hierarchical trees(SPIHT) and Modified SPIHT is done. The comparison shows that Modified SPIHT offers better compression than basic SPIHT and JPEG. The results will help application developers to choose a good wavelet compression system for their applications.
Fast Full Search for Block Matching Algorithmsijsrd.com
This project introduces configurable motion estimation architecture for a wide range of fast block-matching algorithms (BMAs). Contemporary motion estimation architectures are either too rigid for multiple BMAs or the flexibility in them is implemented at the cost of reduced performance. In block-based motion estimation, a block-matching algorithm (BMA) searches for the best matching block for the current macro block from the reference frame. During the searching procedure, the checking point yielding the minimum block distortion (MBD) determines the displacement of the best matching block.
Smartphone indoor positioning based on enhanced BLE beacon multi-laterationTELKOMNIKA JOURNAL
In this paper, we introduce a smartphone indoor positioning method using bluetooth low energy (BLE) beacon multilateration. At first, based on signal strength analysis, we construct a distance calculation model for BLE beacons. Then, with the aims to improve positioning accuracy, we propose an improved lateral method (range-based method) which is applied for 4 nearby beacons. The method is intended to design a real-time system for some services such as emergency assistance, personal localization and tracking, location-based advertising and marketing, etc. Experimental results show that the proposed method achieves high accuracy when compared with the state of the art lateral methods such as geometry-based (conventional trilateration), least square estimation-based (LSE-based) and weighted LSE-based.
This document proposes a new shape-adaptive reversible integer lapped transform (SA-RLT) method for region-of-interest (ROI) coding of 2D remote sensing images. SA-RLT performs better than other transforms like SA-DWT and SA-DCT. The method segments the ROI using a new algorithm rather than hand segmentation. It then designs a SA-RLT based ROI compression scheme using object-based set partitioned embedded block coding (OBSPECK). Experimental results show that SA-RLT compression outperforms DCT and DWT compression for remote sensing images. The method provides flexible bit rate control and allows lossless ROI coding without background areas.
SVM Based Saliency Map Technique for Reducing Time Complexity in HEVCIRJET Journal
This document proposes a technique using support vector machine-based saliency maps to reduce computational complexity in HEVC video coding. It describes saliency detection methods that identify prominent regions of images and discusses implementing an SVM-based saliency detection algorithm in MATLAB. The results show transmitting time is significantly reduced compared to full search, reducing complexity while maintaining comparable compression efficiency.
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.
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.
Survey on Various Image Denoising TechniquesIRJET Journal
This document summarizes several techniques for image denoising. It begins by defining image noise and explaining how noise degrades image quality. It then reviews 7 different published techniques for image denoising, summarizing the key aspects of each technique. These include methods using local spectral component decomposition, SVD-based denoising, patch-based near-optimal denoising, LPG-PCA denoising, trivariate shrinkage filtering, SURE-LET denoising, and 3D transform-domain collaborative filtering. The document concludes that LSCD provides better denoising results according to PSNR analysis and provides an overview of the state-of-the-art in image denoising techniques.
High Speed Data Exchange Algorithm in Telemedicine with Wavelet based on 4D M...Dr. Amarjeet Singh
Existing Medical imaging techniques such as fMRI, positron emission tomography (PET), dynamic 3D ultrasound and dynamic computerized tomography yield large amounts of four-dimensional sets. 4D medical data sets are the series of volumetric images netted in time, large in size and demand a great of assets for storage and transmission. Here, in this paper, we present a method wherein 3D image is taken and Discrete Wavelet Transform(DWT) and Dual-Tree Complex Wavelet Transform(DTCWT) techniques are applied separately on it and the image is split into sub-bands. The encoding and decoding are done using 3D-SPIHT, at different bit per pixels(bpp). The reconstructed image is synthesized using Inverse DWT technique. The quality of the compressed image has been evaluated using some factors such as Mean Square Error(MSE) and Peak-Signal to Noise Ratio (PSNR).
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.
A Novel Blind SR Method to Improve the Spatial Resolution of Real Life Video ...IRJET Journal
This document proposes a novel blind super resolution method to improve the spatial resolution of real-life video sequences. The key aspects of the proposed method are:
1) It estimates blur without knowing the point spread function or noise statistics using a non-uniform interpolation super resolution method and multi-scale processing.
2) It uses a cost function with fidelity and regularization terms of a Huber-Markov random field to preserve edges and fine details in the reconstructed high resolution frames.
3) It performs masking to suppress artifacts from inaccurate motions, adaptively weighting the fidelity term at each iteration for faster convergence.
The method is tested on real-life videos with complex motions, objects, and brightness changes, showing
Leading water utility company in USA was facing a challenge to improve pipeline inspection process to reduce human errors and manual inspection time.Pipeline Anomaly Detection automates the process of identification of defects in pipeline videos, by a camera which notes the observations and lastly it generates the report.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
The document discusses video compression using the Set Partitioning in Hierarchical Trees (SPIHT) algorithm and neural networks. It presents the principles of SPIHT coding and the backpropagation algorithm for neural networks. Various neural network training algorithms are tested for compressing video frames, including gradient descent with momentum and adaptive learning. The results show the compressed frames with different algorithms, and gradient descent with momentum and adaptive learning achieved the best compression ratio of 1.1737089:1 while maintaining image clarity.
Video Content Identification using Video Signature: SurveyIRJET Journal
This document summarizes previous research on video content identification using video signatures. It discusses three types of video signatures (spatial, temporal, and spatio-temporal) that have been used to generate unique descriptors to identify identical video scenes. The document then reviews several existing methods for video signature extraction and matching, including techniques based on ordinal signatures, motion signatures, color histograms, local descriptors using interest points, and compressed video shot matching using dominant color profiles. It concludes by proposing a new temporal signature-based method that aims to accurately detect a video segment embedded in a longer unrelated video by extracting frame-level features, generating fine and coarse signatures, and performing frame-by-frame signature matching.
An unsupervised method for real time video shot segmentationcsandit
Segmentation of a video into its constituent shots
is a fundamental task for indexing and
analysis in content based video retrieval systems.
In this paper, a novel approach is presented
for accurately detecting the shot boundaries in rea
l time video streams, without any a priori
knowledge about the content or type of the video. T
he edges of objects in a video frame are
detected using a spatio-temporal fuzzy hostility in
dex. These edges are treated as features of the
frame. The correlation between the features is comp
uted for successive incoming frames of the
video. The mean and standard deviation of the corre
lation values obtained are updated as new
video frames are streamed in. This is done to dynam
ically set the threshold value using the
three-sigma rule for detecting the shot boundary (a
brupt transition). A look back mechanism
forms an important part of the proposed algorithm t
o detect any missed hard cuts, especially
during the start of the video. The proposed method
is shown to be applicable for online video
analysis and summarization systems. In an experimen
tal evaluation on a heterogeneous test set,
consisting of videos from sports, movie songs and m
usic albums, the proposed method achieves
99.24% recall and 99.35% precision on the average
An unsupervised method for real time video shot segmentationcsandit
Segmentation of a video into its constituent shots is a fundamental task for indexing and
analysis in content based video retrieval systems. In this paper, a novel approach is presented
for accurately detecting the shot boundaries in real time video streams, without any a priori
knowledge about the content or type of the video. The edges of objects in a video frame are
detected using a spatio-temporal fuzzy hostility index. These edges are treated as features of the
frame. The correlation between the features is computed for successive incoming frames of the
video. The mean and standard deviation of the correlation values obtained are updated as new
video frames are streamed in. This is done to dynamically set the threshold value using the
three-sigma rule for detecting the shot boundary (abrupt transition). A look back mechanism
forms an important part of the proposed algorithm to detect any missed hard cuts, especially
during the start of the video. The proposed method is shown to be applicable for online video
analysis and summarization systems. In an experimental evaluation on a heterogeneous test set,
consisting of videos from sports, movie songs and music albums, the proposed method achieves
99.24% recall and 99.35% precision on the average.
Comparative Study of Various Algorithms for Detection of Fades in Video Seque...theijes
In the multimedia environment, digital data has gained more importance in daily routine. Large volume of videos such as entertainment video, news video, cartoon video, sports video is accessed by masses to accomplish their different needs. In the field of video processing Shot boundary detection is current research area. Shot boundary detection has vast impact on effective browsing and retrieving, searching of video. It serves as the beginning to construct the content of videos. Video processing technology has crucial job to provide valid information from videos without loss of any information. This paper is a survey of various novel algorithm for detecting fade-in and fade-out used by renowned personals with different methods. This survey also emphasizes on different core concepts underlying the different detection schemes for the most used video transition effect: fades
Comparitive Analysis for Pre-Processing of Images and Videos using Histogram ...IRJET Journal
This document compares and analyzes the performance of histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE), and gamma correction methods for pre-processing and enhancing poor quality images and videos. It presents the results of applying these methods to 10 images and 10 videos, evaluating the enhanced outputs using peak signal-to-noise ratio (PSNR) values. The results show that CLAHE_RGB performed best for images, improving quality as measured by higher PSNR scores, while gamma correction was most effective for pre-processing images before enhancement. For videos, CLAHE achieved higher PSNR values than HE or gamma correction, indicating it is the most suitable technique.
This document discusses techniques for effective compression of digital video. It introduces several key algorithms used in video compression, including discrete cosine transform (DCT) for spatial redundancy reduction, motion estimation (ME) for temporal redundancy reduction, and embedded zerotree wavelet (EZW) transforms. DCT is used to compress individual video frames by removing spatial correlations within frames. Motion estimation compares blocks of pixels between frames to find and encode motion vectors rather than full pixel values, reducing file size. Combined, these techniques can achieve high compression ratios while maintaining high video quality for storage and transmission.
Event recognition image & video segmentationeSAT Journals
Abstract This paper gives a clear look at the segmentation process at the basic level. Segmentation is done at multiple levels so that we will get different results. Segmentation of relative motion descriptors gives a clear picture about the segmentation done for a given input video. Relative motion computation and histograms incrementation are used to evaluate this approach. Also here we will give complete information about the related research which is done about how segmentation can be done for the both images and videos. Keywords: Image Segmentation, Video Segmentation.
motion and feature based person tracking in survillance videosshiva kumar cheruku
The document summarizes and compares two common algorithms for person tracking in surveillance videos: background subtraction and frame difference. It then proposes a moving target detection algorithm based on background subtraction with a dynamic background. The background image is updated over time through superimposition of the current frame with the previous background image. This allows objects that remain stationary for a period of time to become part of the background. Experimental results showed this algorithm can detect and extract moving targets more effectively and precisely.
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.
In this paper, we propose a novel fast video search algorithm for large video database.
Histogram of Oriented Gradients (HOG) has been reported which can be reliably applied to
object detection, especially pedestrian detection. We use HOG based features as a feature
vector of a frame image in this study. Combined with active search, a temporal pruning
algorithm, fast and robust video search can be achieved. The proposed search algorithm has
been evaluated by 6 hours of video to search for given 200 video clips which each length is 15
seconds. Experimental results show the proposed algorithm can detect the similar video clip
more accurately and robust against Gaussian noise than conventional fast video search
algorithm.
A FAST SEARCH ALGORITHM FOR LARGE VIDEO DATABASE USING HOG BASED FEATUREScscpconf
The document describes a fast video search algorithm that uses Histogram of Oriented Gradients (HOG) features. HOG features are extracted from frames to create feature vectors. These feature vectors are then quantized and histograms are generated for query and database videos. The algorithm uses an active search approach where histogram similarity is calculated and videos are skipped if dissimilar, improving search speed. The algorithm was tested on a 6 hour video database and achieved a search time of 70ms, over 6 times faster than a conventional approach, and was more robust to noise.
A FAST SEARCH ALGORITHM FOR LARGE VIDEO DATABASE USING HOG BASED FEATUREScscpconf
In this paper, we propose a novel fast video search algorithm for large video database. Histogram of Oriented Gradients (HOG) has been reported which can be reliably applied to object detection, especially pedestrian detection. We use HOG based features as a feature vector of a frame image in this study. Combined with active search, a temporal pruning algorithm, fast and robust video search can be achieved. The proposed search algorithm has been evaluated by 6 hours of video to search for given 200 video clips which each length is 15 seconds. Experimental results show the proposed algorithm can detect the similar video clip more accurately and robust against Gaussian noise than conventional fast video search algorithm.
A Smart Target Detection System using Fuzzy Logic and Background SubtractionIRJET Journal
The document describes a smart target detection system that uses fuzzy logic and background subtraction to detect targets in static videos. It first separates the background from input video frames using a fuzzy logic model. It then identifies moving objects and determines if they are cars or humans based on aspect ratio. The system was able to accurately detect and identify cars and humans as targets by applying fuzzy logic to background modeling and subtraction. Some limitations included lack of detection if there is low contrast between the target and background.
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.
Design and Analysis of Quantization Based Low Bit Rate Encoding Systemijtsrd
This document summarizes research on developing a low bit rate encoding system for video compression using vector quantization. It first discusses how vector quantization can achieve high compression ratios and has been used widely in image and speech coding. It then describes the methodology used, which involves taking video frames as input, downsampling the frames to extract pixels, applying vector quantization, and detecting edges on the compressed frames to check compression quality. Finally, it discusses the results of testing the approach on MATLAB and presents conclusions on the advantages of the proposed algorithm for very low bit rate video coding applications.
A Novel Approach for Tracking with Implicit Video Shot DetectionIOSR Journals
1) The document presents a novel approach that combines video shot detection and object tracking using a particle filter to create an efficient tracking algorithm with implicit shot detection.
2) It uses a robust pixel difference method for shot detection that is resistant to sudden illumination changes. It then applies a particle filter for tracking that uses color histograms and Bhattacharyya distance to track objects across frames.
3) The key innovation is that the tracking algorithm is only initiated after a shot change is detected, reducing computational costs by discarding unneeded frames and triggering tracking only when needed. This provides a more efficient solution for tracking large video datasets with minimal preprocessing.
This document presents a method for extracting key frames from videos using discrete wavelet transform (DWT) statistics. It begins with background on video frames, scene changes, and DWT wavelet frequency components. It then describes the proposed key frame extraction algorithm, which involves: 1) applying DWT to consecutive video frames, 2) calculating differences between detail coefficients, 3) computing mean and standard deviation of differences, 4) setting thresholds based on mean and standard deviation, and 5) identifying frames where two difference values exceed thresholds as key frames. The method is applied to sample videos and parameters are tuned to effectively detect key frames for video summarization.
This document presents a method for extracting key frames from videos using discrete wavelet transform (DWT) statistics. It begins with background on video frames, scene changes, and DWT wavelet frequency components. The proposed method extracts key frames in 4 steps: 1) applying DWT to consecutive frames and calculating differences between detail coefficients, 2) computing mean and standard deviation of differences, 3) estimating thresholds using mean and standard deviation, 4) comparing differences to thresholds to identify key frames where differences exceed thresholds. Experimental results on test videos demonstrate the method can detect key frames to represent scene changes for video summarization.
Flow Trajectory Approach for Human Action RecognitionIRJET Journal
This document proposes a method for human action recognition in videos using scale-invariant feature transform (SIFT) and flow trajectory analysis. The key steps are:
1. Extract SIFT features from each video frame to detect keypoints.
2. Track the keypoints across frames and calculate the magnitude and direction of motion for each keypoint.
3. Analyze the tracked keypoints and their motion parameters to recognize the human action, such as walking, running, etc. occurring in the video.
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Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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1. International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2013): 4.438
National Conference on Knowledge, Innovation in Technology and Engineering (NCKITE), 10-11 April 2015
Kruti Institute of Technology & Engineering (KITE), Raipur, Chhattisgarh, India
Licensed Under Creative Commons Attribution CC BY
A Survey on Different Video Scene Change
Detection Techniques
Dolley Shukla1
, Chandra Shekhar Mithlesh2
, ,Manisha Sharma3
1
Shri shankaracharya College of Engineering and Technology,
Senior Associate Professor Junwani, Bhilai, Dist-Durg, Chhattisgarh 490020, India
2
Shri shankaracharya College of Engineering and Technology,
Junwani, Bhilai, Dist-Durg, Chhattisgarh 490020, India, csmithlesh1987@gmail.com
3
Bhilai Institute of Technology, Professor & Head ( E & Tc )
Durg, Dist-Durg, Chhattisgarh 490020, India, Manihasharma1@rediffmail.com
Abstract: A number of automated scene change detection methods for indexing a video sequence to facilitate browsing and retrieval
have been proposed in recent years. Scene change detection plays an important role in a number of video applications, including video
indexing, semantic features extraction, multimedia information systems, Video on demand (VOD), digital TV, online
processing(networking) neural network mobile applications services and technologies, cryptography, and in watermarking. for copy
protection watermarking is an important technique. Recent advances in technology have made tremendous amount of multimedia
content available. The amount of video is increasing, due to which the systems that improve the access to the video is needed. A current
research topic on video includes video abstraction or summarization, video classification, video annotation and content based video
retrieval. In all these applications one needs to identify shot and key frames in video which will correctly and briefly indicates the
contents of video.
Keywords: Video, Video Sequence, False Detection, Detection Rate, SCD, Recall & Precision, PSNR, Macroblocks etc.
1. Introduction
Video is arguably the most popular means of communication
and entertainment [3]. Video is an important component of
the collaboration data streams. In order to enable filtering of
on-line streams as well as effective retrieval of desired data
from an archive, the video/audio streams need to have
annotations or tags generated that describe the “content” of
the stream[5]. To create indexed video databases, video
sequences are first segmented into scenes, a process that is
referred to as scene change detection(SCD) [1]. With the
rapid growth of video indexing, editing and transcoding
system, video analysis and segmentation become more
important than ever before. Scene change detection plays an
indispensable role in segmenting a long video sequence into
smaller unit for further processing.
A video shot is a sequence of successive frames with similar
visual content in the same physical location. Therefore, abrupt
scene change can be identified when there is a large amount
of visual content change across two successive frames while
gradual transition is much difficult to detect since the
successive frame difference during transition is substantially
reduced[7].
2. Terms of Scene Detection
Image:- Images may be two-dimensional, such as photograph ,
screen display, and as well as a three-dimensional, such as
hologram. They may be captured by optical such as cameras,
mirror, lenses, telescope etc.
Picture Element :- The pixel (a word invented from "picture
element") is the basic unit of programmable color on a computer
display or in a computer image
Video:- A typical video sequence its organized into a
sequence of group of pictures(GOP).A video can be broken
down in scene, shot and frames. each GOP consists of one I
(intra-coded) and a few
P(predicted) and B (bi-directionally interpolated) frames as
shown in Figure 1
DISPLAY ORDER
B
P
P
P
B
B
Figure 1: Group Of Picture (GOP)
I-Picture:-A I-picture is intra-frame coded and does not
depend on any previous or future frames. The bit-rate of a I-
picture depends on the visual content of the picture. If the
picture is of high activity, then the bit-rate will be high.
When a scene change occurs between two consecutive I-
pictures, the content of the two I-pictures will be totally
different. The transition may be from high-activity to low-
activity, low-activity to high-activity, or with similar activity.
Therefore, the bit-rate of the I-picture following the scene
change will be increased, decreased or remained similarly.
This implies that we cannot guarantee to detect all cut points
214
2. International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2013): 4.438
National Conference on Knowledge, Innovation in Technology and Engineering (NCKITE), 10-11 April 2015
Kruti Institute of Technology & Engineering (KITE), Raipur, Chhattisgarh, India
Licensed Under Creative Commons Attribution CC BY
if we only look for the significant change in total bit-rate of
two consecutive I-pictures. However, when two pictures are
of different content, they will have different local statistics
even if their global characteristics are similar. Therefore, if
we measure the bit-rate difference at macroblock level, we
can extract the change of image content between two
consecutive frames of sequence. A large change in bit-rate at
macroblock level between two consecutive I-pictures means
that there is a scene change between them.
P-Picture :-P-picture is a forward motion-predicted frame,
which depends on the previous P- or I-picture. When a scene
change occurs between two consecutive P-pictures, it is
difficult to make the forward prediction since the content of
the current frame will be totally different from the previous
one. Most of the macroblocks have so large prediction error
that they will be encoded using the intra-coded mode. This
will of course lead to a significant increase in the bit-rate of
the current frame. Therefore, we can detect the scene change
by checking whether there is a large increment in the bit-rate
of two consecutive P-frames.
B-Picture :-B-picture is a bidirectional-predicted frame,
which depends on both the previous and future P- or I-picture.
When a scene change occurs, the content of the current frame
will have large difference with the previous anchor frame but
have similar content with the future one. Therefore, most of
the macroblocks depend mostly on the future P- or I-picture
and will be backward predicted. Since the prediction can only
be done mostly in one direction, the coding efficiency of the
motion compensation will be lower and it will lead to the
increase in the bit-rate when compared with the previous B-
picture. However, such a increment will not be as significant
as that occurred in I- or P-picture.
Scene :-A scene is a logical grouping of shots into a semantic
unit.
Shot :-A shot is a sequence of frames captured by a single
camera in a single continuous action. A shot boundary is the
transition between two shots.
Frame:- A digital video consists of frames that are a single
frame consists of pixels.
Precision and Recall :-"Precision" defines how reliable the
detected by the algorithm is while "Recall" defines the overall
performance of the algorithm. Result are reported using F-
measure which is combine precision and recall in a single
measure. These measure are computed by
𝑅𝑒𝑐𝑎𝑙𝑙 =
𝑐𝑜𝑟𝑟𝑒𝑐𝑡
𝑐𝑜𝑟𝑟𝑒𝑐𝑡 + 𝑓𝑎𝑙𝑠𝑒
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =
𝑐𝑜𝑟𝑟𝑒𝑐𝑡
𝑐𝑜𝑟𝑟𝑒𝑐𝑡 + 𝑚𝑖𝑠𝑠𝑒𝑑
𝐹 = 2 ×
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 × 𝑟𝑒𝑐𝑎𝑙𝑙
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 + 𝑟𝑒𝑐𝑎𝑙𝑙
PSNR :Peak signal-to-noise ratio is the important parameter
of scene change detection algorithm, efficient SCD algorithm
having the value of PSNR greater than 35. All of the SCD
techniques gives approximately same PSNR result or increase
PSNR by at least same.
3. Review on Scene Change Detection
The survey of various research papers that have contributed to
solve the problem of scene change detection in video
sequences, there is a growing demand of video indexing,
scene browsing and retrievals in Signal Process.
The comparison and analysis of scene change detection
algorithms is described in the following subsections
Ralph M. Ford et al.(1997) presented a five metrics for scene
change detection in video sequences. Metric previously
applied to this problem are surveyed, and are quantitatively
compared to the new metrics. The proposed metrics are
superior. These metrics were also applied to the detection of
gradual transitions. y is a good global metric for detecting
abrupt cuts. [1]
Limitations
The F-test and vi-3 perform the best overall for abrupt cuts,
but require more computation time.
Global metric did not perform well for gradual transitions,
but the statistic based metrics did.
Taehwan Shin et al.(1998) presented a hierarchical scene
change detection in an mpeg-2 compressed video sequence.
In this paper, we propose an efficient scene change detection
algorithm for direct processing of MPEG-2 video bitstreams.
The proposed algorithm utilizes the hierarchical structure of
the compressed bitstreams and statistical characteristics of the
coded parameters, thus greatly reducing computational
requirement compared to pixel domain processing with full
decompression. Occurrence of scene change is checked first
in a COP level, and if the result is affirmative it is checked
again in lower levels : sub-GOP and each picture.We used
several metrics for different levels : variance of DC images
for I-pictures, number of macroblock types for P-pictures and
motion vector types for B pictures. The proposed algorithm
uses such features of the video which are easily extracted by
minimal decoding of the input bitstream that very efficient
processing is achieved. [2]
Limitations
In this algorithm, scene change is checked in a hierarchical
fashion from GOP to sub-GOP and picture level, thus
reducing much of the processing requirements.
W.A.C. Fernando et al. (1999) presented a novel algorithm for
wipe scene change detection in video sequences. In the proposed
scheme, each image in the sequence is mapped to a reduced
image. Then statistical features and structural properties of the
images were used to identify wipe transition region. Finally,
Hough transform is used to analyze the wiping pattern and the
direction of wiping .The algorithm is capable of detecting all wipe
regions accurately even when the video sequence contains other
special effects like fading, dissolving, panning the proposed
algorithm can be used in uncompressed video to detect wipe
regions with a very high reliability. [3]
215
3. International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2013): 4.438
National Conference on Knowledge, Innovation in Technology and Engineering (NCKITE), 10-11 April 2015
Kruti Institute of Technology & Engineering (KITE), Raipur, Chhattisgarh, India
Licensed Under Creative Commons Attribution CC BY
Limitations
Further work is required to extend this algorithm for
compressed video.
W.A.C. Fernando, et al. (2000) presented a unified approach to
scene change detection in uncompressed.There is an urgent need
to extract key information automatically from video for the
purposes of indexing, fast retrieval and scene analysis. To support
this vision, reliable scene change detection algorithms must be
developed. Results on video of various content types are reported
and validated with the proposed scheme in uncompressed and
MPEG-2 compressed video. The accuracy of the detected
transitions is above 95% and 90% for uncompressed and MPEG-2
compressed video respectively. The proposed algorithm can be
used in uncompressed and compressed video to detect scene
changes with a high reliability. The algorithm is capable in of
detecting all scene changes accurately. [4]
Limitations
Future work is required to extend the algorithm for camera
movements detection within the same framework.
Wensheng Zhou, et al.(2000) proposed an On-line Scene
Change Detection of Multicast Video. Network-based
computing is becoming an increasingly important area of
research, whereby computational elements within a
distributed infrastructure process/enhance the data that
traverses through its path. These computations as online
processing and this paper investigates scene change detection
in the context of MB one based proxies in the network. On-
line processing varies from traditional offline processing
schemes, where for example, the whole video scope is
known as a priori, which allows multiple scans of the stored
video files during video processing. The proposed algorithms
do scene change detection and extract key frames from video
bitstreams sent through the mbone network. Several
algorithms based on histogram differences and evaluate them
with respect to precision, recall, and processing latency a few
effective methods of on-line video scene change detection
over MBone video for the purposes of annotation/filtering.
Main advantages of our algorithms is their ability to support
real-time video processing on the network.Joint algorithms
based on video codec characteristics for acquiring fast and
accurate scene detection were carried out. Both global color
histograms were extracted and block information of DCT
coding was used.. Algorithms were capable of satisfying on-
line annotation needs. The algorithm was tested on different
data bandwidths & gives the best performance in each
case.[5]
Limitations
Algorithms were mainly targeting real-time video scene
analysis, improving processing speed and reducing latency
are two more issues of concern, which makes it improper to
compare the proposed algorithm with other off-line scene
detection techniques, such as those mentioned in the
Related Work section, where video data are processed off-
line.
In the future, they plan to study more video on-line
annotations based on video content, such as key frame
classification. Because of the complicated nature of video
processing, they plan on using multiple workstations
working in parallel to realize more sophisticated real-time
annotation
Plan to study algorithms that are tolerant of packet losses in
the network.
The on-line processing system is designed to meet the
requirements of real-time video multicasting over the
Internet and to utilize the successful video parsing
techniques available today.
Shu-Ching Chen,et al.(2002 ) presented a change detection
by audio and video clues. Automatic video scene change
detection is a challenging task. Using audio or visual
information alone often cannot provide a satisfactory
solution. However, how to combine audio and visual
information efficiently still remains a difficult issue since
there are various cases in their relationship due to the
versatility of videos. Shu-ching presented an effective scene
change detection method that adopts the joint evaluation of
the audio and visual features. First, video information is used
to find the shot boundaries. Second, the audio features for
each video shot can be extracted. Lastly, an audio-video
combination schema is proposed to detect the video scene
boundaries. Unlike the traditional methods that first analyze
audio and video data separately and then combine them, we
analyze them at different phases The audio feature extraction
is based on the detected video shots, which tends to be more
stable and more reliable in charactering the audio data. The
experimental results demonstrate that our method performs
very well in terms of precision and recall value.[6]
Xiaoquan Yi et al.(2005) presented a Fast Pixel-Based
Video Scene Change Detection. This paper proposes a new
simple and efficient method to detect abrupt scene change
based on only pixel values. Conventional pixel-based
techniques can produce a significant number of false
detections and missed detections when high motion and
brightness variations are present in the video. To increase
scene change detection accuracy yet maintaining a low
computational complexity, a two-phase strategy is developed.
Frames are firstly tested against the mean absolute frame
differences (MAFD) via a relaxed threshold, which rejects
about 90% of the non-scene change frames. The rest 10% of
the frames are then normalized via a histogram equalization
process. A top-down approach is applied to refine the
decision via four features: MAFD and three other features
based on normalized pixel values - signed difference of mean
absolute frame difference (SDMAFD*), absolute difference
of frame variance (ADFV*), and mean absolute frame
differences (MAFD*) method contributes to higher detection
rate and lower missed detection rate while maintaining a low
computational complexity, which is attractive for real-time
video applications. Method is relatively immune from sharp
illumination changes, moving objects, camera motion, and
other similar effects because of the well combined metrics.
Furthermore, our method uses only frame pixel values
without any motion estimation processes so that frugal
computational complexity is maintained, which makes it very
attractive for real-time video applications. [7]
Limitations
This technique was simple but it performs poorly in cases
of high motions of objects, high motions of cameras, or
severe scene luminance variations (i.e., flicker)
216
4. International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2013): 4.438
National Conference on Knowledge, Innovation in Technology and Engineering (NCKITE), 10-11 April 2015
Kruti Institute of Technology & Engineering (KITE), Raipur, Chhattisgarh, India
Licensed Under Creative Commons Attribution CC BY
Gao, J. Jiang et al.(2006) presented a PCA-based approach
for video scene change detection on compressed video. An
automatic, real-time detection approach to video scene
change detection is presented. Owing to the high correlation
of two consecutive video frames, it is proposed that only the
eigenvector corresponding to the largest eigenvalue is
retained in the principal component analysis (PCA) for video
data. A one-dimensional PCA feature of video data is then
generated from the PCA. It shows superior performance
compared to the histogram feature and the pixel feature.The
detection algorithm based on this PCA feature is then
designed to detect both abrupt and gradual transitions. The
proposed approach is tested on the TREC video test
repository to validate its performance. The superior
performance of the new PCA feature. Then our change
detection algorithm is based on the PCA feature to detect the
scene change. [8]
Limitations
Tested on the TREC video test repository to validation for
its performance.
J.-R. Ding et al. (2007) presented adaptive group-of-pictures
and scene change detection methods based on existing h.264
advanced video coding information. The H.264 advanced
video coding (H.264/AVC) standard provides several
advanced features such as improved coding efficiency and
error robustness for video storage and transmission. In order
to improve the coding performance of H.264/AVC, coding
control parameters such as group-of-pictures (GOP) sizes
should be adaptively adjusted according to different video
content variations (VCVs), which can be extracted from
temporal deviation between two consecutive frames. The
authors present a simple VCV estimation to design adaptive
GOP detection (AGD) and scene change detection (SCD)
methods by using the obtained motion information, where the
motion vectors and the sum of absolute transformed
differences as VCV features are effectively used to design the
AGD and SCD algorithms, respectively. In order to avoid
unnecessary computation, the above VCV features were
obtained only in the 4 x 4 inter-frame prediction mode.The
proposed AGD with SCD methods can increase the peak
signal-to-noise ratio by 0.62 dB on average over the
H.264/AVC operated with a fixed GOP size. Besides, the
proposed SCD method reached a scene change detection rate
of 98% .On the other hand, if the scene-changed frame
without any detection mechanism is coded by interceding . It
will become inefficient and waste considerable computation
time in motion estimation. and founded that the GOP size for
H.264/AVC should be appropriately decreased and increased
for higher and lower VCV frames, respectively, to improve
the coding performance. The method successfully utilized the
existing video coding information, such as motion vectors and
motion residuals, to become an effective VCV feature. Both
the proposed AGD and SCD methods can effectively help to
adjust a better GOP size to improve the coding performance of
H.264/AVC. the proposed joint AGD and SCD schemes can
increase PSNR by at least 0.62 dB compared with the
H.264/AVC operated in the fixed GOP size on average. [9]
Limitations
The proposed and some existing VCV characteristics can
be further extended for adaptive search window, adaptive
search methods (of three step search and
ZHANG Ji et al.(2007) presented an effective error
concealment framework for h.264 decoder based on video
scene change detection. In this paper, we propose an effective
error concealment framework for H.264 decoder based on the
scene change detection. The proposed framework quickly
and accurately detects whether scene change occurs in the
decoding frame, based on the detection result, both corrupted
intra frames and damaged inter frames can be reconstructed
by spatial or improved temporal EC (Error Concealment)
algorithm. The experiment shows that, compared with the
traditional error concealment method in the H.264/A VC non
normative decoder, the proposed framework has better
robustness and can efficiently improve the visual quality and
PSNR of the decoded video. [10]
Limitations
The final experimental results demonstrate that our
framework can apparently improve the visual quality of
reconstructed video sequence and PSNR.
In the future, they will emphasize more on exploring the
scene change algorithm for B frames and improve spatial
or temporal EC algorithms in the new frame work.
Jens Brandt et al.(2008) presented a fast frame-based scene
change detection in the compressed domain for mpeg-4
video. Detection of scene changes is an elementary step in
automatic video processing like indexing, segmentation or
transcoding Video indexing and segmentation allow fast
browsing without decoding the complete video. In the case of
transcending, the information about scene changes such as
cuts and fades as well as about special movements like
rotations or zooms in video frames is helpful to determine
suitable transcoding parameters. In the compressed domain
only information about DCT values as well as motion
information can be used to determine such scene changes and
movements. Therefore different measures were defined
which use the encoded DCT values and motion vectors of
each compressed frame. Based on these measures as well as
on motion vector histograms, a fast approach to detect
different kinds of scene changes and special movements in
MPEG-4 videos in the compressed domain was proposed. In
this paper we have presented a fast frame-based algorithm for
compressed domain scene change detection in MPEG-4
video streams. The easy computation of the used metrics and
histograms makes the whole scene change detection
algorithm very fast which is very important for real time
video processing. Therefore the scene change detection can
only be a tool to indicate a stronger or weaker correlation
towards one or the other scene movement. However, the
evaluation results show that the algorithm detects a high
number of scene changes and movements. Based on these
promising results Jens Brandt developed a method to use the
information about detected scene changes and special
movements within a video stream for determining suitable
transcoding parameters. [11]
217
5. International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2013): 4.438
National Conference on Knowledge, Innovation in Technology and Engineering (NCKITE), 10-11 April 2015
Kruti Institute of Technology & Engineering (KITE), Raipur, Chhattisgarh, India
Licensed Under Creative Commons Attribution CC BY
Limitations
The highly statistical nature of the scene change detection
results in a statement that by design cannot be absolutely
correct.
Hakan Haberdar et al.(2010) presented a disparity map
refinement for video based scene change detection using a
mobile stereo camera platform. This paper presents a novel
disparity map refinement method and vision based
surveillance framework for the task of detecting objects of
interest in dynamic outdoor environments from two stereo
video sequences taken at different times and from different
viewing angles by a mobile camera platform. Preliminary
disparity images were refined based on estimated disparities
in neighboring frames. Segmentation is performed to
estimate ground planes, which in turn are used for
establishing spatial registration between the two video
sequences. Finally, the regions of change are detected using
the combination of texture and intensity gradient features.
We present experiments on detection of objects of different
sizes and textures in real videos. The framework is evaluated
on 4 real video sequences and the experimental results show
that the proposed framework is able to detect changes/objects
of different sizes and textures, and is able to ascertain if there
are no changes in the scene, thereby minimizing false
positives. [12]
Limitations
This method need for future work and it will focus on
relaxing our assumption on known temporal alignment and
improving the spatial registration module.
Ufuk Sakarya et al.(2010) presented a video scene detection
using graph-based representations. One way of realizing this
step is to obtain shot or scene information. One or more
consecutive semantically correlated shots sharing the same
content construct video scenes. On the other hand, video
scenes are different from the shots in the sense of their
boundary definitions; video scenes have semantic boundaries
and shots are defined with physical boundaries. In this paper,
we concentrate on developing a fast, as well as well-
performed video scene detection method. Our graph partition
based video scene boundary detection approach, in which
multiple features extracted from the video, determines the
video scene boundaries through an unsupervised clustering
procedure. For each video shot to shot comparison feature, a
one-dimensional signal is constructed by graph partitions
obtained from the similarity matrix in a temporal interval.
After each one-dimensional signal is filtered, an unsupervised
clustering is conducted for finding video scene boundaries.We
adopt two different graph-based approaches in a single
framework in order to find video scene boundaries. The
proposed graph-based video scene boundary detection method
is evaluated and compared with the graph-based video scene
detection method presented in literature. [13]
Limitations
Some issues are considered into account for future research
directions. Different video features can be used in order to
improve the results and; moreover, content varying based
changes in the multiple features may increase the scene
detection performance.
Especially, feature weighting in clustering process can
also be applied. In addition, an adaptation of the proposed
method for other types of videos, like news,
documentaries, etc., is another research direction.
Ankita P. Chauhan1 et al (2013) presented Hybrid
Approach for Video Compression Based on Scene Change
Detection. In order to fulfill the requirement of limited
channel bandwidth and of growing video demand like
streaming media delivery on internet, and digital library,
video compression is necessary. In video compression,
temporal redundancy between adjacent frames is removed
with block based motion estimation algorithms. Video
represents a sequence of frames captured from camera. Scene
is a series of consecutive frames captured from narrative
point of view. In this paper we present an effective scene
change detection method for an uncompressed video. We
have divided frames in to blocks and applied a canny edge
detector in consecutive frames. Count no of pixels (ones) in
each block and compare it with consecutive frames. If scene
change happens then number of pixels per block will change,
based on that change we can detect scene change in
consecutive frame. presented a hybrid approach, in which we
have used scene change detection along with block based
motion estimation algorithms (BME) to compress video.In
this paper we have presented a simple approach with high
accuracy. To reduce the number of computations, hybrid
approach along with scene change detection have been
implemented.[14]
Limitations
Simulation result shown that Proposed Hybrid approach
compared with NTSS, 4SS and DS algorithm, it reduces
the cost of computational complexity and also gives
approximately same PSNR result.
Neetirajsinh J. Chhasatia et al. (2013) presented a
Performance Evaluation of Localized Person Based Scene
Detection and Retrieval in Video. A work regarding person
localization using novel approach had been carried out.
Person localization is the first step in all the surveillance
application in multimedia and human-computer interface
applications. The objective of was to address a system for a
person localization system which described important tasks
of video content analysis, namely, video segmentation,
localizing the person and their identification. A novel
framework was proposed for combined video segmentation
and retrieval of scene with object/person localization. In
which the dominant regions as a large area of the frame have
been tracked throughout a shot and then stable features were
extracted. Dominant region based effective parameters
(DREP) model which includes low level features of the
proposed model. The low level features were referenced as
the color of dominant region, edges, area, centroid and
position of a particular object or person to be localized in the
video. Based on the extracted information dimensional
processing was used to extract images of faces or the
required objects. The simulated results based on the
presented algorithm shows good performance & highly
robust to camera, objects motions and could withstand
illumination changes even in different poses in the sequences
of the images.[15]
218
6. International Journal of Science and Research (IJSR)
ISSN (Online): 2319-7064
Index Copernicus Value (2013): 6.14 | Impact Factor (2013): 4.438
National Conference on Knowledge, Innovation in Technology and Engineering (NCKITE), 10-11 April 2015
Kruti Institute of Technology & Engineering (KITE), Raipur, Chhattisgarh, India
Licensed Under Creative Commons Attribution CC BY
Limitations
Improvement in its basic inputs would result in better
performance of all these techniques.
4. Conclusion
The above survey of various researchers of different
algorithms of Scene Change Detection shows that the two
Main problem associated with xisting algorithms i.e.
threshold-dependent algorithms and color Histogram
Method.
The threshold-dependent algorithm gives false detection
with scenes involving fast camera or object motion. Color
Histogram Method having some advantages like Efficient
representation, Easy computation, Global color distribution,
Insensitive to Rotation, Zooming Changing resolution and
Partial occlusions. Bu some disadvantages also like
Ignorance to spatial relationship, Sensitive in illumination
changes and Chooses illumination-insensitive color
channels. The method avoided the false alarms by using the
validation mechanism. It also proves that the statistical
model-based approach is reliable for gradual scene-change
detection. A very high detection rate is achieved while the
false alarm rate is comparatively low. For better output
detection, a scene change detection algorithm should be
proposed with the properties low false alarm,high
robustness in gradual transition, camera fabrication, special
events and so on . For a considerably good quality of scene
change detection (SCD) algorithm PSNR ranges should be
>40, Recall & precision rate > 95% and change detection rate
>98%.
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Author Profile
Chandra Shekhar Mithlesh received
the B.E. degrees from government
Engineering College jagdalpur in
Electronics & Telecommunication with
first Class and currently perusing his
master degree in Communication
Engineering from Shri shankaracharya
College of Engineering and Technology,, Bhilai,, Chhattisgarh.
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