Fast pattern matching in Walsh-Hadamard domain and its application in video processing.

Li Ngai.Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.Includes bibliographical references.Abstracts in English and Chinese.Chapter Chapter 1. --- Introduction --- p.1-1Chapter 1.1. --- A Brief Review on Pattern Matching --- p.1-1Chapter 1.2. --- Objective of the Research Work --- p.1-5Chapter 1.3. --- Organization of the Thesis --- p.1-6Chapter 1.4. --- Notes on Publications --- p.1-7Chapter Chapter 2. --- Background Information --- p.2-1Chapter 2.1. --- Introduction --- p.2-1Chapter 2.2. --- Review of Block Based Pattern Matching --- p.2-3Chapter 2.2.1 --- Gradient Descent Strategy --- p.2-3Chapter 2.2.2 --- Simplified Matching Operations --- p.2-10Chapter 2.2.3 --- Fast Full-Search Methods --- p.2-14Chapter 2.2.4 --- Transform-domain Manipulations --- p.2-19Chapter Chapter 3. --- Statistical Rejection Threshold for Pattern Matching --- p.3-1Chapter 3.1. --- Introduction --- p.3-1Chapter 3.2. --- Walsh Hadamard Transform --- p.3-3Chapter 3.3. --- Coarse-to-fine Pattern Matching in Walsh Hadamard Domain --- p.3-4Chapter 3.3.1. --- Bounding Euclidean Distance in Walsh Hadamard Domain --- p.3-5Chapter 3.3.2. --- Fast Projection Scheme --- p.3-9Chapter 3.3.3. --- Using the Projection Scheme for Pattern Matching --- p.3-17Chapter 3.4. --- Statistical Rejection Threshold --- p.3-18Chapter 3.5. --- Experimental Results --- p.3-22Chapter 3.6. --- Conclusions --- p.3-29Chapter 3.7. --- Notes on Publication --- p.3-30Chapter Chapter 4. --- Fast Walsh Search --- p.4-1Chapter 4.1. --- Introduction --- p.4-1Chapter 4.2. --- Approximating Sum-of-absolute Difference Using PS AD --- p.4-3Chapter 4.3. --- Two-level Threshold Scheme --- p.4-6Chapter 4.4. --- Block Matching Using SADDCC --- p.4-10Chapter 4.5. --- Optimization of Threshold and Number of Coefficients in PSAD --- p.4-15Chapter 4.6. --- Candidate Elimination by the Mean of PSAD --- p.4-23Chapter 4.7. --- Computation Requirement --- p.4-28Chapter 4.8. --- Experimental Results --- p.4-32Chapter 4.9. --- Conclusions --- p.4-45Chapter 4.10. --- Notes on Publications --- p.4-46Chapter Chapter 5. --- Conclusions & Future Works --- p.5-1Chapter 5.1. --- Contributions and Conclusions --- p.5-1Chapter 5.1.1. --- Statistical Rejection Threshold for Pattern Matching --- p.5-2Chapter 5.1.2. --- Fast Walsh Search --- p.5-3Chapter 5.2. --- Future Works --- p.5-4References --- p.

Investigation of Some Image and Video Coding Techniques

Image compression refers to the process of reducing the quantity of data used to represent digital images, and is a combination of spatial image compression and temporal motion compensation. Spatial image compression is done by exploiting the spatial redundancy. Temporal motion compensation is done by exploiting the correlation of the pixels in the nearby frame. Images require substantial storage and transmission resources, thus image compression is advantageous to reduce these requirements. The report covers some background of wavelet analysis, data compression and how wavelets have been and can be used for image compression and some of the block matching techniques for motion estimation. In this thesis, investigations have been made to understand the actual mechanism of compression of still images and applying the principle to the video frames. Initially image compression is analyzed using wavelet transform and then it is implemented. In later stages motion estimation techniques are analyzed so as to achieve compression by exploiting the temporal redundancy. Six algorithms for motion estimation are analyzed and compared with each other through their results

Fast block matching motion estimation algorithms for video compression

As the telecommunication technology grows in the modern era from internet to video conferencing, Video compression has become an avoidable feature in information broadcast and also in the entertainment media. In this thesis we compared a different block matching motion estimation algorithms to find the motion estimation with a rapid growth of multimedia information; when transmitting a large amount of data video coding standards have become crucial. Motion estimation ascertain to be the key to splendid performance in video coding by recover the temporal redundancy effectively between adjacent frames, so it has been widely used to popular video compression coding standards such as MPEG-2, MPEG-4 and recent video coding standards H.264 of video data for storage and transmission. So Based on the study of motion vector distribution from several commonly used test image sequences, a three step diamond search [TSDS]algorithm for fast block matching motion estimation is proposed in this paper .The performance of this algorithm is compared with other existing algorithms of basic full search [FS], three step search [TSS] and diamond search [DS] by means of error metrics and no of search points in this the simulation results shows that the proposed three step diamond search algorithm achieves close performance with that of diamond search [DS] and uses less no of search points than the three step search[TSS]. When compared with original diamond search [DS] algorithm, this algorithm requires less computation time and gives an improved performance

Development of Fast Motion Estimation Algorithms for Video Comression

With the increasing popularity of technologies such as Internet streaming video and video conferencing, video compression has became an essential component of broadcast and entertainment media. Motion Estimation (ME) and compensation techniques, which can eliminate temporal redundancy between adjacent frames effectively, have been widely applied to popular video compression coding standards such as MPEG-2, MPEG-4. Traditional fast block matching algorithms are easily trapped into the local minima resulting in degradation on video quality to some extent after decoding. Since Evolutionary Computing Techniques are suitable for achieving global optimal solution, these techniques are introduced to do Motion Estimation procedure in this thesis. Zero Motion prejudgement is also included which aims at finding static macroblocks (MB) which do not need to perform remaining search thus reduces the computational cost. Simulation results obtained show that the proposed Clonal Particle Swarm Optimization algorithm given a very good improvement in reducing the computations overhead and achieves very good Peak Signal to Noise Ratio (PSNR) values, which makes the techniques more efficient than the conventional searching algorithms. To reduce the Motion vector overhead in Bidirectional frame prediction, in this thesis novel Bidirectional Motion Estimation algorithm based on PSO is also proposed and results shows that the proposed method can significantly reduces the computational complexity involved in the Bidirectional frame prediction and also least prediction error in all video sequence

Surveillance centric coding

PhDThe research work presented in this thesis focuses on the development of techniques specific to surveillance videos for efficient video compression with higher processing speed. The Scalable Video Coding (SVC) techniques are explored to achieve higher compression efficiency. The framework of SVC is modified to support Surveillance Centric Coding (SCC). Motion estimation techniques specific to surveillance videos are proposed in order to speed up the compression process of the SCC. The main contributions of the research work presented in this thesis are divided into two groups (i) Efficient Compression and (ii) Efficient Motion Estimation. The paradigm of Surveillance Centric Coding (SCC) is introduced, in which coding aims to achieve bit-rate optimisation and adaptation of surveillance videos for storing and transmission purposes. In the proposed approach the SCC encoder communicates with the Video Content Analysis (VCA) module that detects events of interest in video captured by the CCTV. Bit-rate optimisation and adaptation are achieved by exploiting the scalability properties of the employed codec. Time segments containing events relevant to surveillance application are encoded using high spatiotemporal resolution and quality while the irrelevant portions from the surveillance standpoint are encoded at low spatio-temporal resolution and / or quality. Thanks to the scalability of the resulting compressed bit-stream, additional bit-rate adaptation is possible; for instance for the transmission purposes. Experimental evaluation showed that significant reduction in bit-rate can be achieved by the proposed approach without loss of information relevant to surveillance applications. In addition to more optimal compression strategy, novel approaches to performing efficient motion estimation specific to surveillance videos are proposed and implemented with experimental results. A real-time background subtractor is used to detect the presence of any motion activity in the sequence. Different approaches for selective motion estimation, GOP based, Frame based and Block based, are implemented. In the former, motion estimation is performed for the whole group of pictures (GOP) only when a moving object is detected for any frame of the GOP. iii While for the Frame based approach; each frame is tested for the motion activity and consequently for selective motion estimation. The selective motion estimation approach is further explored at a lower level as Block based selective motion estimation. Experimental evaluation showed that significant reduction in computational complexity can be achieved by applying the proposed strategy. In addition to selective motion estimation, a tracker based motion estimation and fast full search using multiple reference frames has been proposed for the surveillance videos. Extensive testing on different surveillance videos shows benefits of application of proposed approaches to achieve the goals of the SCC

A survey on video compression fast block matching algorithms

Video compression is the process of reducing the amount of data required to represent digital video while preserving an acceptable video quality. Recent studies on video compression have focused on multimedia transmission, videophones, teleconferencing, high definition television, CD-ROM storage, etc. The idea of compression techniques is to remove the redundant information that exists in the video sequences. Motion compensation predictive coding is the main coding tool for removing temporal redundancy of video sequences and it typically accounts for 50–80% of video encoding complexity. This technique has been adopted by all of the existing International Video Coding Standards. It assumes that the current frame can be locally modelled as a translation of the reference frames. The practical and widely method used to carry out motion compensated prediction is block matching algorithm. In this method, video frames are divided into a set of non-overlapped macroblocks and compared with the search area in the reference frame in order to find the best matching macroblock. This will carry out displacement vectors that stipulate the movement of the macroblocks from one location to another in the reference frame. Checking all these locations is called Full Search, which provides the best result. However, this algorithm suffers from long computational time, which necessitates improvement. Several methods of Fast Block Matching algorithm are developed to reduce the computation complexity. This paper focuses on a survey for two video compression techniques: the first is called the lossless block matching algorithm process, in which the computational time required to determine the matching macroblock of the Full Search is decreased while the resolution of the predicted frames is the same as for the Full Search. The second is called lossy block matching algorithm process, which reduces the computational complexity effectively but the search result's quality is not the same as for the Full Search

Classification-Based Adaptive Search Algorithm for Video Motion Estimation

A video sequence consists of a series of frames. In order to compress the video for efficient storage and transmission, the temporal redundancy among adjacent frames must be exploited. A frame is selected as reference frame and subsequent frames are predicted from the reference frame using a technique known as motion estimation. Real videos contain a mixture of motions with slow and fast contents. Among block matching motion estimation algorithms, the full search algorithm is known for its superiority in the performance over other matching techniques. However, this method is computationally very extensive. Several fast block matching algorithms (FBMAs) have been proposed in the literature with the aim to reduce computational costs while maintaining desired quality performance, but all these methods are considered to be sub-optimal. No fixed fast block matching algorithm can effi- ciently remove temporal redundancy of video sequences with wide motion contents. Adaptive fast block matching algorithm, called classification based adaptive search (CBAS) has been proposed. A Bayes classifier is applied to classify the motions into slow and fast categories. Accordingly, appropriate search strategy is applied for each class. The algorithm switches between different search patterns according to the content of motions within video frames. The proposed technique outperforms conventional stand-alone fast block matching methods in terms of both peak signal to noise ratio (PSNR) and computational complexity. In addition, a new hierarchical method for detecting and classifying shot boundaries in video sequences is proposed which is based on information theoretic classification (ITC). ITC relies on likelihood of class label transmission of a data point to the data points in its vicinity. ITC focuses on maximizing the global transmission of true class labels and classify the frames into classes of cuts and non-cuts. Applying the same rule, the non-cut frames are also classified into two categories of arbitrary shot frames and gradual transition frames. CBAS is applied on the proposed shot detection method to handle camera or object motions. Experimental evidence demonstrates that our method can detect shot breaks with high accuracy

ENHANCED COMPUTATION TIME FOR FAST BLOCK MATCHING ALGORITHM

Video compression is the process of reducing the amount of data required to represent digital video while preserving an acceptable video quality. Recent studies on video compression have focused on multimedia transmission, videophones, teleconferencing, high definition television (HDTV), CD-ROM storage, etc. The idea of compression techniques is to remove the redundant information that exists in the video sequences. Motion compensated predictive coding is the main coding tool for removing temporal redundancy of video sequences and it typically accounts for 50-80% of the video encoding complexity. This technique has been adopted by all of the existing international video coding standards. It assumes that the current frame can be locally modelled as a translation of the reference frames. The practical and widely method used to carry out motion compensated prediction is block matching algorithm. In this method, video frames are divided into a set of non-overlapped macroblocks; each target macroblock of the current frame is compared with the search area in the reference frame in order to find the best matching macroblock. This will carry out displacement vectors that stipulate the movement of the macroblocks from one location to another in the reference frame. Checking all these locations is called full Search, which provides the best result. However, this algorithm suffers from long computational time, which necessitates improvement. Several methods of Fast Block Matching algorithm were developed to reduce the computation complexity. This thesis focuses on two classifications: the first is called the lossless block matching algorithm process, in which the computational time required to determine the matching macroblock of the full search is decreased while the resolution of the predicted frames is the same as for the full search. The second is called the lossy block matching algorithm process, which reduces the computational complexity effectively but the search result’s quality is not the same as for the full search