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

Block matching algorithm based on Harmony Search optimization for motion estimation

Motion estimation is one of the major problems in developing video coding applications. Among all motion estimation approaches, Block-matching (BM) algorithms are the most popular methods due to their effectiveness and simplicity for both software and hardware implementations. A BM approach assumes that the movement of pixels within a defined region of the current frame can be modeled as a translation of pixels contained in the previous frame. In this procedure, the motion vector is obtained by minimizing a certain matching metric that is produced for the current frame over a determined search window from the previous frame. Unfortunately, the evaluation of such matching measurement is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be viewed as an optimization problem whose goal is to find the best-matching block within a search space. The simplest available BM method is the Full Search Algorithm (FSA) which finds the most accurate motion vector through an exhaustive computation of all the elements of the search space. Recently, several fast BM algorithms have been proposed to reduce the search positions by calculating only a fixed subset of motion vectors despite lowering its accuracy. On the other hand, the Harmony Search (HS) algorithm is a population-based optimization method that is inspired by the music improvisation process in which a musician searches for harmony and continues to polish the pitches to obtain a better harmony. In this paper, a new BM algorithm that combines HS with a fitness approximation model is proposed. The approach uses motion vectors belonging to the search window as potential solutions. A fitness function evaluates the matching quality of each motion vector candidate.Comment: 25 Pages. arXiv admin note: substantial text overlap with arXiv:1405.472

Coherent video reconstruction with motion estimation at the decoder

In traditional predictive video coding the block matching is performed at the encoder. The obtained motion field is then transmitted to the decoder, together with the prediction residue. Nevertheless, if the motion field is not provided it can be reconstructed, as long as the decoder manages to exploit some correlated information. This paper presents an algorithm for the motion estimation at the decoder side, given the prediction residue only. The main novelty of this algorithm relies on the contextual reconstruction of a frame region composed of several blocks. Simulation results show that taking into account a whole row can improve significantly the results obtained with an algorithm that reconstructs each block separately

Block matching algorithm for motion estimation based on Artificial Bee Colony (ABC)

Block matching (BM) motion estimation plays a very important role in video coding. In a BM approach, image frames in a video sequence are divided into blocks. For each block in the current frame, the best matching block is identified inside a region of the previous frame, aiming to minimize the sum of absolute differences (SAD). Unfortunately, the SAD evaluation is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be approached as an optimization problem, where the goal is to find the best matching block within a search space. The simplest available BM method is the full search algorithm (FSA) which finds the most accurate motion vector through an exhaustive computation of SAD values for all elements of the search window. Recently, several fast BM algorithms have been proposed to reduce the number of SAD operations by calculating only a fixed subset of search locations at the price of poor accuracy. In this paper, a new algorithm based on Artificial Bee Colony (ABC) optimization is proposed to reduce the number of search locations in the BM process. In our algorithm, the computation of search locations is drastically reduced by considering a fitness calculation strategy which indicates when it is feasible to calculate or only estimate new search locations. Since the proposed algorithm does not consider any fixed search pattern or any other movement assumption as most of other BM approaches do, a high probability for finding the true minimum (accurate motion vector) is expected. Conducted simulations show that the proposed method achieves the best balance over other fast BM algorithms, in terms of both estimation accuracy and computational cost.Comment: 22 Pages. arXiv admin note: substantial text overlap with arXiv:1405.4721, arXiv:1406.448

Dynamic Region and Block-Based Motion Estimation for Video Compression

The aim of this project is to find a motion estimation method that works in combination with block matching in order to reduce the visible artifacts. The proposed solution tries to extract the real motions taking place in a sequence. The developed algorithm is a region based motion estimator. We associate to regions of general shape motion parameters which describe an ane transformation on the plane. The traditional block matching is then used for smaller transformations. The developed algorithm is iterative. It alternatively considers the motion and the region as being constant and renes the other one according to this hypothesis. The results will show that it converges and leads to good results if big objects are moving in a sequence

Single step optimal block matched motion estimation with motion vectors having arbitrary pixel precisions

This paper proposes a non-linear block matched motion model with motion vectors having arbitrary pixel precisions. The optimal motion vector which minimizes the mean square error is solved analytically in a single step. Our proposed algorithm can be regarded as a generalization of conventional half pixel search algorithms and quarter pixel search algorithms because our proposed algorithm could achieve motion vectors with arbitrary pixel precisions. Also, the computational effort of our proposed algorithm is lower than that of conventional quarter pixel search algorithms because our proposed algorithm could achieve motion vectors in a single step

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