Block-based Motion Estimation Technique Research Articles

VLSI Architecture of Block Matching Algorithms for Motion Estimation in High Efficiency Video Coding

High-efficiency video coding (HEVC) is a latest video coding standard and the motion estimation unit is the most important block. The work presents the different types of Matching Criteria for Block-Based Motion Estimation technique in HEVC standard. HEVC requires fast motion estimation algorithms to have better real time performance. The hardware implementation of motion estimation helps to achieve high speed though parallel processing. An improved block matching technique is designed with reduced blocks for HEVC. The proposed method has less execution time where only blocks having motion are compared for prediction computation. The searching time complexity is dependent on the number of blocks that are having motion. The searching time of frame having small motion can be reduced to 80–85% as compared to the traditional full search algorithm. In the paper, sum of absolute difference, mean square error and mean absolute difference are computed to find the best matching algorithm for HEVC. However, SAD has less computational complexity with compare to other matching criteria. The results suggest that proposed motion estimation algorithm has better performance with compare to similar previous work.

Enhanced dynamic pattern search algorithm with weighted search points for fast motion estimation

For video encoding, a number of block-based motion estimation techniques have been proposed in the literature. Some of them are based on fixed search patterns, whereas others are utilizing spatial and temporal correlations of blocks within video frames. It has been found that the block correlations give an initial lead in identification of motion vector of the candidate block and the possibility of oversearch or undersearch is minimized. In this paper, the dynamic pattern search (DPS) algorithm for block-based motion estimation has been enhanced which reduces the search point computation by first identifying the stationary blocks. Further, in the proposed algorithm, search points within the search area are evaluated for minimum distortion point in prioritized fashion to reduce the possibility of being trapped in local minima. The proposed work has been compared experimentally with fixed search pattern techniques like full search, diamond search (DS) and hexagon search and adaptive search pattern techniques like adaptive rood pattern search and DPS in terms of various performance parameters. It has been found that the proposed technique is faster by 6.32 and 3.30 times than DS and DPS techniques in terms of avg. search point computation per block and produces better peak signal to noise ratio than DS and DPS by the value of 1.03 and 0.63 dB, respectively. In terms of structural similarity measurement index value, enhanced DPS gives 8% better results than DPS and performs better compression than any other technique except FS.

Block Matching Algorithm for Motion Estimation using Previous Motion Vector Pattern

motion estimation is major concern in developing video coding applications. Among various available motion estimation techniques block matching is most favorites because of its effectiveness and simplicity for hardware and software both. In block based motion estimation techniques, computation is reduced by limiting the number of candidate search points within the search window or by implementing effective search criterion for picking the correct block. Previous pattern based motion estimation algorithm has been suggested in literature which controls the number of search points drastically and hence reduce the computation cost. In this manuscript, a new block based searching method is proposed that uses the motion vector for the just before calculated block as the motion vector for the current block and directly map the block in referenced frame accordingly. Experimental results show that an increase up to 15% in terms of peak signal to noise ratio (PSNR) has been achieved than conventional block matching algorithm with almost same execution time. Further, in terms of quality/computation ratio where quality and computation has been measured in terms of PSNR and execution time respectively, proposed method has 10-20% gain over existing block based technique.

Block-based Motion Estimation in Video Frames using Artificial Neural Networks: A Selective Review

Nowadays, we are very frequently transmitting the video over internet. This is due to an extensive increase in multimedia applications over hand held devices, such as smart mobile phones and also other advance conventional devices. Motion Estimation is an important field of study in the area of motion analysis and motion compression. The motion estimation is done by using two basic approaches, namely, pixel-based motion estimation and block-based motion estimation. Here we have proposed a detailed study literature survey and review of the block-based estimation methods in detail. This paper presents a comprehensive review of block based motion estimation techniques which plays a vital role in multimedia transmission over public network. The advantage of this review paper is to find the absolute optimal solution.

Bio-Inspired Neural Networks for Block Based Motion Estimation

In the multi-view video coding, both temporal and interview redundancies can be exploited by using standard block-based motion estimation technique. This paper describes a novel Bio-inspired neural networks model to enhance regions and extract contours of image for block-based motion estimation. We implements the optimized algorithm in the reference model of H.264 compiled by VC6.0, and chooses six typical video sequences for simulation comparison. Experiments performed show the good visual results which can reduces the computational complexity in a certain degree and enhances encoding efficiency with few changes in the reconstructed image quality and bit rate.

Fast Variable Padding Motion Estimation Using Smart Zero Motion Prejudgment Technique for Pixel and Frequency Domains

Motion estimation (ME) plays an important role in modern video coders since it consumes approximately 60-80% of the entire encoder's computations. In this paper, three novel techniques are proposed to effectively speed up the ME process. First, a smart prediction technique for effectively deciding an initial search center is proposed. Second, a zero motion prejudgment technique is proposed to accurately decide whether the pre-estimated ISC can be considered as a best match motion vector (MV) and consequently save the required computations for the MV refinement process. Finally, a variable padding pixels ME technique is proposed to adaptively determine the number of padding pixels required for the search window for more computational cost savings. The three techniques are combined and applied to the block-based ME for a superior computational complexity savings in the ME process. The performance of the proposed techniques is tested in both the pixel domain ME and the frequency domain ME in terms of their quantitative visual quality (peak signal-to-noise ratio, PSNR), their computational complexity, and their bit rate. Experimental results demonstrate that the proposed fast ME technique is able to achieve approximately a 99.4% reduction in ME time compared to the conventional full search block-based ME (FSSBB-ME) with negligible degradation in both the PSNR and the bit rate. Additionally, the experimental results prove the effectiveness of the proposed techniques if they are combined with any block-based ME technique such as the fast extended diamond enhanced predictive zonal search. Experimental results also demonstrate that there is at least an additional savings of 72% in ME time using the conventional discrete cosine transform phase correlation ME (DCT-PC-ME) in the frequency domain compared to the conventional FSBB-ME technique in pixel domain. Compared to the conventional DCT-PC-ME, applying the proposed novel techniques to the DCT-PC-ME saves up to 89% in ME time.

A Fast Block Matching Algorithm Based on Motion Vector Correlation and Integral Projections

The block based motion estimation technique is adopted by various video coding standards to reduce the temporal redundancy in video sequences. The core of that technique is the search algorithm implementedto find the location of the best matched block. Indeed, the full search algorithm is the most straightforward and optimal but computationally demanding search algorithm. Consequently, many fast and suboptimal search algorithms have been proposed. Reduction of the number of location being searched is the approach used to decrease the computational load of full search. In this paper, hybridization between an adaptive search algorithm and the full search algorithm is proposed. The adaptive search algorithm benefits from the correlation within spatial and temporal adjacent blocks. At the same time, a feature domain based matching criteria is used to reduce the complexity resulting from applying the pixel based conventional criteria. It is shown that the proposed algorithm produces good quality performance and requires less computational time compared with popular block matching algorithms.

Filtering of Block Motion Vectors for Use in Motion-Based Video Indexing and Retrieval

Though block-based motion estimation techniques are primarily designed for video coding applications, they are increasingly being used in other video analysis applications due to their simplicity and ease of implementation. The major drawback associated with these techniques is that noises, in the form of false motion vectors, cannot be avoided while capturing block motion vectors. Similar noises may further be 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. This paper presents a new technique for capturing large number of true object motion vectors by eliminating the false motion vector fields, for use in the application of object motion based video indexing and retrieval applications. Experimental results prove that our proposed technique significantly increases the percentage of retained true object motion vectors while eliminating all false motion vectors for variety of standard and non-standard video sequences.