A Novel Search Technique of Motion Estimation for Video Compression

Video Compression is highly demanded now a days as due to the fact that in the field of entertainment, medicine and communication there is high demand for digital video technology. For the effective removal of temporal redundancy between the frames for better video compression Motion estimation techniques plays a major role. Block based motion estimation has been widely used for video coding. One such method is the Hierarchical Search Technique for BMA. By amalgamating the three different search algorithms like New three step search, New Full search and New Cross diamond search a novel hierarchical search methodology is proposed. Sub- sampling the original image into additional two levels is done and thereby the New Diamond search algorithm and a new three-step search algorithm are used in the bottom two levels and the Full Search is performed on the highest level where the complexity is relatively low. In terms of PSNR with reduced complexity this new proposed algorithm showed better performance

Block Based Motion Vector Estimation Using FUHS16, UHDS16 and UHDS8 Algorithms for Video Sequence

Block-matching algorithm is the most common technique applied in block-based motion estimation technique. There are several block-matching algorithm based on block-based motion estimation techniques have been developed. Full search (FS), three step search (TSS), new three step search (NTSS), diamond search (DS) and hexagon based search (HS) are the most well known block-matching algorithm. These techniques are applied to video sequences to remove the temporal redundancy for compression purposes and to gauge the motion vector estimation. In addition, the mentioned block-matching algorithms are the baseline techniques that have been used to further develop all the enhanced or improved algorithms. In order to develop the proposed methods, the baseline techniques are studied to develop the proposed algorithms. This chapter proposes modelling of fast unrestricted hexagon search (FUHS16) and unrestricted hexagon-diamond search (UHDS16) algorithms for motion vector estimation, which is based on the theory and application of block-based motion estimation. Both of these algorithms are designed using 16 × 16 block size. In particular, the motion vector estimation, quality performance, computational complexity, and elapsed processing time are emphasised. These parameters have been used to measure the experimental results. It is the aim of this study that this work provides a common framework with which to evaluate and understand block-based matching motion estimation performance. On the theoretical side, four fundamental issues are explored: (1) division of frame, (2) basic block-based matching, (3) motion vector estimation, and (4) block-matching algorithm development. Various existing block-matching motion estimation algorithms have been analysed to develop the fundamental research. Based on the theoretical and fundamental research analysis the FUHS16 and UHDS16 algorithms using 16 × 16 block-based motion estimation formulations were developed. To improve the UHDS16 algorithm, 8 × 8 block-matching technique has been tested. The 8 × 8 block-matching technique is known as UHDS8. The results show positive improvements. From an application perspective, the UHDS8 algorithm efficiently captured the motion vectors in many video sequences. For example, in video compression, the use of motion vectors on individual macro-blocks optimized the motion vector information. The UHDS8 algorithm also offers improvement in terms of image quality performance, computational complexity and elapsed processing time. Thus, this chapter offers contributions in certain areas such as reducing the mechanism of computational complexity in estimating the motion from the video sequences. In particular, the FUHS16, UHDS16 and UHDS8 algorithms were developed to estimate the motion vectors field in the video sequences. Theoretical analysis block-based matching criteria are adapted to FUHS16, UHDS16 and UHDS8 algorithms, which are based on search points technique. Basically, the proposed of FUHS16, UHDS16 and UHDS8 algorithm produces the best motion vector estimation finding based on the block-based matching criteria. Besides that, the UHDS8 algorithm also improves the image quality performances and the search points in terms of the computational complexity. Overall, the study shows that the UHDS8 algorithm produces better results compared to the FUHS16 and UHDS16 algorithm

A Low-complexity Wavelet Based Algorithm for Inter-frame Image Prediction

In this paper, a novel multi-resolution variable block size algorithm (MRVBS) is introduced. It is based on: (1) Using the wavelet components of the seven sub-bands from two layers of wavelet pyramid in the lowest resolution; (2) Performing a block matching estimation within a nine-block only in each sub-band of the lower layer; (3) Scaling the estimated motion vectors and using them as a new search center for the finest resolution. The motivation for using the multi-resolution approach is the inherent structure of the wavelet representation. A multi-resolution scheme significantly reduces the searching time, and provides a smooth motion vector field. The approach presented in this paper providing an accurate motion estimate even in the presence of single and mixed noise. As a part of this framework, a comparison of the Full search (FS) algorithm, the three-step search (TSS) algorithm and the new algorithm (MRVBS) is presented. For a small addition in computational complexity over a simple TSS algorithm, the new algorithm achieves good results in the presence of noise

Center of Mass-Based Adaptive Fast Block Motion Estimation

This work presents an efficient adaptive algorithm based on center of mass (CEM) for fast block motion estimation. Binary transform, subsampling, and horizontal/vertical projection techniques are also proposed. As the conventional CEM calculation is computationally intensive, binary transform and subsampling approaches are proposed to simplify CEM calculation; the binary transform center of mass (BITCEM) is then derived. The BITCEM motion types are classified by percentage of (0, 0) BITCEM motion vectors. Adaptive search patterns are allocated according to the BITCEM moving direction and the BITCEM motion type. Moreover, the BITCEM motion vector is utilized as the initial search point for near-still or slow BITCEM motion types. To support the variable block sizes, the horizontal/vertical projections of a binary transformed macroblock are utilized to determine whether the block requires segmentation. Experimental results indicate that the proposed algorithm is better than the five conventional algorithms, that is, three-step search (TSS), new three-step search (N3SS), four three-step search (4SS), block-based gradient decent search (BBGDS), and diamond search (DS), in terms of speed or picture quality for eight benchmark sequences

Modified Three-Step Search Block Matching Motion Estimation and Weighted Finite Automata based Fractal Video Compression

The major challenge with fractal image/video coding technique is that, it requires more encoding time. Therefore, how to reduce the encoding time is the research component remains in the fractal coding. Block matching motion estimation algorithms are used, to reduce the computations performed in the process of encoding. The objective of the proposed work is to develop an approach for video coding using modified three step search (MTSS) block matching algorithm and weighted finite automata (WFA) coding with a specific focus on reducing the encoding time. The MTSS block matching algorithm are used for computing motion vectors between the two frames i.e. displacement of pixels and WFA is used for the coding as it behaves like the Fractal Coding (FC). WFA represents an image (frame or motion compensated prediction error) based on the idea of fractal that the image has self-similarity in itself. The self-similarity is sought from the symmetry of an image, so the encoding algorithm divides an image into multi-levels of quad-tree segmentations and creates an automaton from the sub-images. The proposed MTSS block matching algorithm is based on the combination of rectangular and hexagonal search pattern and compared with the existing New Three-Step Search (NTSS), Three-Step Search (TSS), and Efficient Three-Step Search (ETSS) block matching estimation algorithm. The performance of the proposed MTSS block matching algorithm is evaluated on the basis of performance evaluation parameters i.e. mean absolute difference (MAD) and average search points required per frame. Mean of absolute difference (MAD) distortion function is used as the block distortion measure (BDM). Finally, developed approaches namely, MTSS and WFA, MTSS and FC, and Plane FC (applied on every frame) are compared with each other. The experimentations are carried out on the standard uncompressed video databases, namely, akiyo, bus, mobile, suzie, traffic, football, soccer, ice etc. Developed approaches are compared on the basis of performance evaluation parameters, namely, encoding time, decoding time, compression ratio and Peak Signal to Noise Ratio (PSNR). The video compression using MTSS and WFA coding performs better than MTSS and fractal coding, and frame by frame fractal coding in terms of achieving reduced encoding time and better quality of video

Adaptive Diamond Search Algorithm for Motion Estimation

Implementation of the Block Matching Algorithm (BMA) in Motion Estimation (ME) has been widely used in video encoder due to its simplicity and high compression efficiency. Many fast search methods of BMAs are being introduced to increase the efficiency  of the ME  process. This paper proposed a new algorithm, namely Adaptive Diamond Search Algorithm (ADS) which employs three different search patterns for its two main stages. At the initial step, an additional step is added to a predetermined static block to further speed up the search process as it is beneficial to small motion video sequence contents. The performances of the ADS are then compared with three selected established algorithms, namely the Full Search (FS), Diamond Search (DS) and Hexagon-Diamond Search (HDS). Based on the simulation result, the proposed algorithm yields a very good video quality performance with fewer search points compared with other algorithms

Performance Analysis of Hexagon-Diamond Search Algorithm for Motion Estimation using MATLAB

To achieve a high compression ratio in coding video data, a method known as Motion Estimation (ME) is often applied to reduce the temporal redundancy between successive frames of a video sequence. One of ME techniques, known as Block Matching Algorithm (BMA), has been widely used in various video coding standards. In recent years, many of these BMAs have been developed with similar intention of reducing the computational costs while at the same time maintaining the video signal quality. In this paper, an algorithm called Hexagon-Diamond Search (HDS) is proposed for ME where the algorithm and several fast BMAs, namely Three Step Search (TSS), New Three Step Search (NTSS), Four Step Search (4SS) as well as Diamond Search (DS), are first selected to be implemented onto various type of standard test video sequence using MATLAB before their performances are compared and analyzed in terms of peak signal-to-noise ratio (PSNR), number of search points needed as well as their computational complexity. Simulation results demonstrate that HDS algorithm has speed up other algorithm’s computational work up to 56% while at the same time maintains close performance in terms of PSNR to others

Performance Analysis of Hexagon-Diamond Search Algorithm for Motion Estimation

To achieve a high compression ratio in coding video data, a method known as Motion Estimation (ME) is often applied to reduce the temporal redundancy between successive frames of a video sequence. One of ME techniques, known as Block Matching Algorithm (BMA), has been widely used in various video coding standards. In recent years, many of these BMAs have been developed with similar intention of reducing the computational costs while at the same time maintaining the video signal quality. In this paper, an algorithm called Hexagon-Diamond Search (HDS) is proposed for ME where the algorithm and several fast BMAs, namely Three Step Search (TSS), New Three Step Search (NTSS), Four Step Search (4SS) as well as Diamond Search (DS), are first selected to be implemented onto various type of standard test video sequence using MATLAB before their performances are compared and analyzed in terms of peak signal-to-noise ratio (PSNR), number of search points needed as well as their computational complexity. Simulation results demonstrate that HDS algorithm has speed up other algorithm’s computational work up to 56% on average while at the same time maintains close performance in terms of average PSNR to others