Motion estimation and video coding

Over the last ten years. research on the analysis of visual motion has come to play a key role in the fields of data compression for visual communication as well as computer vision. Enormous efforts have been made on the design of various motion estimation algorithms. One of the fundamental tasks in motion estimation is the accurate measurement of 2-D dense motion fields. For this purpose. we devise and present in this dissertation a multiattribute feedback computational framework. In this framework for each pixel in an image. instead of a single image intensity. multiple image attributes are computed as conservation information. To enhance the estimation accuracy. feedback technique is applied. Besides. the proposed algorithm needs less differentiation and thus is more robust to various noises. With these features. the estimation accuracy is improved considerably. Experiments have demonstrated that the proposed algorithm outperforms most of the existing techniques that compute 2-D dense motion fields in terms of accuracy. The estimation of 2-D block motion vector fields has been dominant among techniques in exploiting the temporal redundancy in video coding owing to its straightforward implementation and reasonable performance. But block matching is still a computational burden in real time video compression. Hence. efficient block matching techniques remain in demand. Existing block matching methods including full search and multiresolution techniques treat every region in an image domain indiscriminately no matter whether the region contains complicated motion or not. Motivated from this observation. we have developed two thresholding techniques for block matching in video coding. in which regions experiencing relatively uniform motion are withheld from further processing via thresholfing. thus saving compu­tation drastically. One is a thresholding multiresolution block matching. Extensive experiments show that the proposed algorithm has a consistent performance for sequences with different motion complexities. It reduces the processing time ranging from 14% to 20% while maintaining almost the same quality of the reconstructed image (only about 0.1 dB loss in PSNR). compared with the fastest existing multiresolution technique. The other is a thresholding hierarchical block matching where no pyramid is actually formed. Experiments indicate that for sequences with less motion such as videoconferencing sequences. this algorithm works faster and has much less motion vectors than the thresholding multiresolution block matching method

Video Coding: Comparison of Block Matching Techniques In Motion Estimation

This project is to investigate the advantages of using the various types of block matching methods as a type of motion estimation techniques compared to encoding each frame as a separate static image. As video is continuous media, it is important to maintain its quality and efficiency while compressing it. As the similarity of a frame and the next frame is great, it can be used as the advantage in video coding. This is because, the background of the image will usually stay the same and the only thing that will change is the moving object in that video. In order to achieve this, the author has to develop a program that can accommodate motion compensation and estimation by utilizing the computational for the motion estimation techniques, if applicable. The author will then have to run the program with some test video sequences to compare the performances of different block matching techniques for different types of video sequences. The major types of block matching techniques are Full (Exhaustive) Search, and a Fast Search (Three Step Search) has been chosen for this project. The author has also chosen to work on Quarter Common Intermediate Format (QCIF) video sequences. As the purpose of this project is to investigate the motion estimation technique, only the video frames will be considered and the sound of the actual video will be left out. Experimental results show that MMSE has a better PSNR value than MAD but consume more time and has higher complexity of operation. Block sizes and window sizes also have a significant effect on the predicted image. The Three Step Search has experiments has shown that it has a higher speed ratio as compared to Full Search, but with reduced quality

Video Coding: Comparison of Block Matching Techniques In Motion Estimation

This project is to investigate the advantages of using the various types of block matching methods as a type of motion estimation techniques compared to encoding each frame as a separate static image. As video is continuous media, it is important to maintain its quality and efficiency while compressing it. As the similarity of a frame and the next frame is great, it can be used as the advantage in video coding. This is because, the background of the image will usually stay the same and the only thing that will change is the moving object in that video. In order to achieve this, the author has to develop a program that can accommodate motion compensation and estimation by utilizing the computational for the motion estimation techniques, if applicable. The author will then have to run the program with some test video sequences to compare the performances of different block matching techniques for different types of video sequences. The major types of block matching techniques are Full (Exhaustive) Search, and a Fast Search (Three Step Search) has been chosen for this project. The author has also chosen to work on Quarter Common Intermediate Format (QCIF) video sequences. As the purpose of this project is to investigate the motion estimation technique, only the video frames will be considered and the sound of the actual video will be left out. Experimental results show that MMSE has a better PSNR value than MAD but consume more time and has higher complexity of operation. Block sizes and window sizes also have a significant effect on the predicted image. The Three Step Search has experiments has shown that it has a higher speed ratio as compared to Full Search, but with reduced quality

A novel filter for block-based motion estimation

Noises, in the form of false motion vectors, cannot be avoided while capturing block motion vectors using block based motion estimation techniques. Similar noises are further introduced when the technique of global motion compensation is applied to obtain 'true' object motion from video sequences, where both the camera and object motions are present. We observe that the performance of the mean and the median filters in removing false motion vectors, for estimating 'true' object motion, is not satisfactory, especially when the size of the object is significantly smaller than the scene. In this paper we introduce a novel filter, named as the Mean-Accumulated-Thresholded (MAT) filter, in order to capture 'true' object motion vectors from video sequences with or without the camera motion (zoom and/or pan). Experimental results on representative standard video sequences are included to establish the superiority of our filter compared with the traditional median and mean filters

An Automated Algorithm for Approximation of Temporal Video Data Using Linear B'EZIER Fitting

This paper presents an efficient method for approximation of temporal video data using linear Bezier fitting. For a given sequence of frames, the proposed method estimates the intensity variations of each pixel in temporal dimension using linear Bezier fitting in Euclidean space. Fitting of each segment ensures upper bound of specified mean squared error. Break and fit criteria is employed to minimize the number of segments required to fit the data. The proposed method is well suitable for lossy compression of temporal video data and automates the fitting process of each pixel. Experimental results show that the proposed method yields good results both in terms of objective and subjective quality measurement parameters without causing any blocking artifacts.Comment: 14 Pages, IJMA 201

Adaptive Multi-Pattern Fast Block-Matching Algorithm Based on Motion Classification Techniques

Motion estimation is the most time-consuming subsystem in a video codec. Thus, more efficient methods of motion estimation should be investigated. Real video sequences usually exhibit a wide-range of motion content as well as different degrees of detail, which become particularly difficult to manage by typical block-matching algorithms. Recent developments in the area of motion estimation have focused on the adaptation to video contents. Adaptive thresholds and multi-pattern search algorithms have shown to achieve good performance when they success to adjust to motion characteristics. This paper proposes an adaptive algorithm, called MCS, that makes use of an especially tailored classifier that detects some motion cues and chooses the search pattern that best fits to them. Specifically, a hierarchical structure of binary linear classifiers is proposed. Our experimental results show that MCS notably reduces the computational cost with respect to an state-of-the-art method while maintaining the qualityPublicad