Motion estimation techniques for digital TV: a review and a new contribution

Motion estimation and compensation techniques are widely used in video coding. This paper addresses the problem of the trade-off between the motion and the prediction error information. Under some realistic hypotheses, the transmission cost of these two components can be estimated. Therefore, we obtain a criterion which controls the motion estimation process in order to optimize its performance. As a particular application, this criterion is applied to the split procedure of an adaptive multigrid block matching technique. Simulation results are presented, showing the significant improvements due to the method.

Video coding standards use the block-matching algorithm (BMA) and motion-compensated prediction to reduce temporal redundancies present in image sequences. Block matching is used since it is computationally efficient and produces a minimal representation of the motion field that is transmitted as side information. In order to build a robust coder the motion-estimation technique must be able to track motion in a noisy source. The approach presented in this paper uses spatio-temporal motion prediction, providing an accurate motion estimate even in the presence of noise. With this approach, noisy sources can be compressed efficiently and robustly in standard video coders (e.g., MPEG-1, MPEG-2, H.261, and H.263) with little increase in complexity.

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.

In this paper, Motion Estimation (ME) and Motion Compensation (MC) techniques are used for video compression which eliminates the temporal redundancy of video sequence. For motion estimation, Block Matching (BM) algorithm has been used among the various motion estimation algorithms because of its simplicity and efficiency. In this paper, the performance of block matching algorithm such as Four Step Search (4SS) has been improved using Block Transform Scaling (BTS). In BTS all the varying nature frames of video transform into uniform frames using adaptive filter, which fills the pixel information where pixel value is missing within the frame. The proposed work mainly focuses on the compression of videos which have medical information in it. At the end of this paper the result analysis has been done using the parameters such as Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), Computation Time (CT) and Compression Ratio (CR).

In recent years, the variable block size (VBS) motion estimation technique has been widely employed to improve the performance of the block matching algorithm (BMA). In VBS, the block size is varied according to the type of motion. The VBS is known to be very efficient for areas containing complex motions. But, it requires a large amount of computation. The computation required is 2 or 5 times compared with the full search BMA. In this paper, a new VBS motion estimation algorithm, called the classified variable block size (CVBS), is proposed to overcome this problem. The algorithm classifies the input blocks into 3 categories: the background, the shade-motion, and the edge-motion. According the characteristics of the classified blocks, various motion estimation techniques are then used to improve the coding performance. The performance of VBS and CVBS under the coding efficiency is investigated. It is shown that the CVBS algorithm requires about 1/6 to 1/10 computations needed by the conventional VBS algorithm.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The rate-constrained (R-D) motion estimation techniques have been presented to improve the conventional block-matching algorithm by using a joint rate and distortion criterion. This paper presents two motion estimation algorithms using Kalman filter to further enhance the performance of the conventional R-D motion estimation at a relative low computational cost. The Kalman filter exploits the correlation of block motion to achieve higher precision of motion estimation and compensation. In the first algorithm, the Kalman filter is utilized as a postprocessing to raise the motion compensation accuracy of the conventional R-D motion estimation. In the second algorithm, the Kalman filter is embedded into the optimization process of R-D motion estimation by defining a new R-D criterion. It further improves the rate-distortion performance significantly.

Stereo image sequence coding based on three-dimensional motion estimation and compensation

Motion estimation is an important component in video compression schemes, which impacts the encoder performance and the quality of the reconstructed video sequence. Block-based techniques are the most popular among the several motion estimation techniques used in practice due to their simplicity allowing efficient hardware implementations. The quality of motion predicted frame and the speed of motion vector computation are the primary measures of performance of motion estimation schemes. The video compression standards only specify the syntax of the bitstream and not any specifics of the motion estimation techniques. Thus, the knowledge of the workings and performance of commonly used motion estimation schemes would facilitate development of better schemes or choosing a particular one for a given application. We present the results of performance evaluation of combinations of several commonly used block-based motion estimation algorithms and distortion measures.

Motion estimation is one of the major bottlenecks in real-time performance scalable video coding applications due to high computational complexity of exhaustive search. To address this, researchers so far focused on low-complexity motion estimation and rate-distortion optimization in isolation. Proliferation of power-constrained handheld devices with image capturing capability has created demand for much smarter approach where motion estimation is integrated with rate control such that rate-distortion-complexity optimization can be effectively achieved. It is indeed crucial to provide such performance scalability in motion estimation to facilitate complexity management in such devices. This chapter presents an overview of motion estimation. Beginning with an introduction to the importance of motion estimation, it systematically examines various motion estimation techniques and their strengths and weaknesses, focussing primarily on block-based motion search. It then examines the limitation of the existing techniques in accommodating performance scalability, introduces a promising approach, Distance-dependent Thresholding Search (DTS) motion search, to fill in this gap, and concludes with future research directions in the field. The authors suggest that the content of the chapter will make a significant contribution and serve as a reference for multimedia signal processing research at postgraduate level.

Motion estimation and compensation techniques are widely used for video coding applications but the real-time motion estimation is not easily achieved due to its enormous computations. In this paper, a new fast motion estimation algorithm based on line search is presented, in which computation complexity is greatly reduced by using the line search strategy and a parallel search pattern. Moreover, the accurate search is achieved because the small square search pattern is used. It has a best-case scenario of only 9 search points, which is 4 search points less than the diamond search algorithm. Simulation results show that, compared with the previous techniques, the LSPS algorithm significantly reduces the computational requirements for finding motion vectors, and also produces close performance in terms of motion compensation errors.

This paper presents some techniques for efficient motion estimation (ME) implementation on fixed-point digital signal processor (DSP) for high resolution video coding. First, challenges in large search-range ME are discussed. Then, based on the statistics analysis of the best matched block, correlations in variable block-size ME are removed on algorithm level while keeping almost the same performance as the original fast strategy. Based on this modification and data reuse technique, this paper proposes an index search method for DSP implementation, which is a good solution to balance both high coding efficiency and high coding speed on the DSP platform. In index search, ME is divided into three steps and highly parallel pipelines are designed for each step. The fully use of pipelines brings about 85% time reduction during ME. Index search can also be easily extended to achieve different range and patterns.

Data-driven respiratory gating techniques in cardiac PET extract respiratory motion signal from the PET data to guide respiratory gating and motion estimation. However, the influence of the quality of the list-mode data including the uptake ratio between the myocardium and background and the detected count level on its performance has never been studied thoroughly. In this project, list-mode PET data derived from realistic Monte Carlo simulation of different uptake ratios and count levels was employed to quantitatively evaluate the respiratory motion signal extraction and estimation techniques. Simulated projection data were generated from the 4D XCAT phantom with known respiratory motion. The list-mode data were rebinned into projection data at 200 msec intervals and the centers-of-mass of a region-of-interest over the heart were calculated. Contributions from the background were subtracted in the centroid calculation. The peak in the Fourier transform of the time sequence of the centroid locations, or the frequency spectrum, was identified as the respiratory motion signal and its SNR was measured. The high frequency components of the frequency spectrum were removed for noise smoothing before extracting the respiratory motion signal for use in respiratory gating estimation. The results show that with background correction, the amplitude of the estimated respiratory motion signal is increased and is closer to the truth. Both lower myocardium to background uptake ratio and lower count level reduce the SNR of the respiratory motion signal. When they become too low, the extraction of the respiratory motion signal becomes difficult or fails completely.

In this study, it is intended to construct a super-resolution image from video images by using motion compensation technique based on block matching and its performance is compared with performances of various motion compensation techniques available in literature for super-resolution. Experimental results show that results of block matching based motion compensation technique are better than results of other motion compensation techniques. Furthermore, a mask which introduces differences between the images to be combined together, is suggested in order to improve the success of motion compensation technique based on block matching, as a result of this image quality has increased remarkably.

Motion estimation using multiple reference frames is widely used as the basis for recent video coding standards (eg. H.264/AVC) to achieve increased coding efficiency. However, this increases the complexity of the encoding process. In this paper, a new technique for efficient motion estimation is proposed. A combination of multiple reference frame selection and image residue-based mode selection is used to improve motion estimation time. By dynamic selection of an initial reference frame in advance, the number of reference frames to be considered is reduced. In addition, from examination of the residue between the current block and reconstructed blocks in preceding frames, variable block size mode decisions are made. Modified initial motion vector estimation and early stop condition detection are also adopted to speed up the motion estimation procedure. Experimental results compare the performance of the proposed algorithm with a state of the art motion estimation algorithm and demonstrate significantly reduced motion estimation time while maintaining PSNR performance.

The applicability of MPEG video coding can be improved by scaling both the algorithmic complexity and resource usage appropriately for the intended device and application. For this purpose, we present a new technique for motion estimation, based on a scalable three-stage process including frame processing in display order, approximation of motion vector fields using multiple references and optional quality refinements. Experiments show that the computational effort is scalable with a factor of 14, resulting in a global variation of 7 dB SNR in picture quality. At full processing, our technique slightly outperforms a 32/spl times/32 full search motion estimation. The technique forms a valuable contribution to mobile MPEG coding applications, following the scalability concepts introduced by Mietens, de With and Hentschel (see IEEE Int. Conf. on Image Proc. (ICIP 2001), vol.3, p.462-465, Oct. 2001).