Fast Motion Estimation Algorithm using Hybrid Search Patterns for Video Streaming Application

The objective of the paper is to develop new block matching Motion Estimation (ME) algorithm using hybrid search patterns along the direction of best match. The search efficiency for sequences with fast motions and high resolutions is improved by proposing New Cross Diagonal-Hexagon Search (NCDHS) algorithm which involves a novel multi half-hexagon grid global search pattern and a cross diagonal-hexagon local search pattern. The new search pattern enables the proposed algorithm to perform better search using 9.068 search points on an average, to obtain optimal motion vector with slight improvement in quality. This inturn reduces ME Time upto 50.11%, 47.12%, 32.99% and 43.28% on average when compared to the existing Diamond Search (DS), Hexagon Search (HS), New Cross Hexagon Search (NHEXS) and Enhanced Diamond Search (EDS) algorithms respectively. The novelty of the algorithm is further achieved by applying the algorithm proposed for live streaming application. The NCDHS algorithm is run on two MATLAB sessions on the same computer by establishing the connection using Transmission Control Protocol (TCP) /Internet Protocol (IP) network. The ME Time obtained is 14.5986 seconds for a block size 16x16, is less when compared to existing algorithms and that makes the NCDHS algorithm suitable for real time streaming application

Review And Comparative Study Of Motion Estimation Techniques To Reduce Complexity

ABSTRACT: Block matching motion estimation is a key Component in video compression because of its high computational complexity. The process of motion estimation has become a bottleneck problem in many video applications. Typical applications include HDTV, multimedia communications, video conferencing, etc. Motion estimation is a useful in estimating the motion of any object. Motion estimation has been conventionally used in the application of video encoding but nowadays researchers from various fields other than video encoding are turning towards motion estimation to solve various real life problems in their respective fields. In this paper, we present a review of block matching based motion estimation algorithms, reduced complexity of motion estimation techniques and a comparative study across all different algorithms. Also the aim of this study is to provide the reader with a feel of the relative performance of the algorithms, with particular attention to the important trade-off between computational complexity, prediction quality, result quality and other various applications. Keywords: Fixed size block motion estimation (FSBME), Block-based motion estimation (BMME), Peak-Signal-toNoise-Ratio (PSNR), Hybrid block matching algorithm (HBMA). I.INTRODUCTION Motion compensated transform coding forms the basis of the existing video compression Standards H.26 1/H.262 and MPEG-1 /MPEG-2, where the compression algorithm tries to exploit the temporal and spatial redundancies by using some form of motion compensation followed by a transform coding, respectively. The key step in removing temporal redundancy is the motion estimation where a motion Vector is predicted between the current frame and a reference frame. Following the motion estimation, a Motion compensation stage is applied to obtain the residual image, i.e. the pixel differences between the current frame and a reference frame. Later this residual is compressed using transform coding or a combination of transform and entropy coding. The above Video compression standards employs block motion estimation techniques. The main advantages of FSBME (fixed size block motion estimation) are simplicity of the algorithm and the fact that no segmentation information needs to be transmitted In block motion compensated video coding; first image frames are divided into square blocks (FIXED SIZE). The next step is to apply a three-step procedure, consisting of Motion Detection, Motion Estimation and Motion Compensation. Motion detection is used for classifying blocks as moving or non-moving based on a predefined distance or similarity measure. This similarity measure is usually done by MSE (minimum mean square error) criteria or minimum SAD (sum of absolute different) criteria. The output of the motion-estimation algorithm comprises the motion vector for each block, and the pixel value differences between the blocks in the current frame and the "matched" blocks in the reference frame. We call this difference signal the motion compensation error, or simply block error. Many techniques have been proposed for motion estimation for video compression so far. All the methods are proposed keeping any one or more of the three directions aimed that 1.reducing computational complexity 2.representing true motion (proving good quality) 3.reducing bit rate(high compression ratio)