Lai-Man Po

Bio: Lai-Man Po is an academic researcher from City University of Hong Kong. The author has contributed to research in topics: Motion estimation & Search algorithm. The author has an hindex of 33, co-authored 199 publications receiving 5608 citations. Previous affiliations of Lai-Man Po include Hong Kong Applied Science and Technology Research Institute & University of Hong Kong.

A novel four-step search algorithm for fast block motion estimation

Abstract: Based on the real world image sequence's characteristic of center-biased motion vector distribution, a new four-step search (4SS) algorithm with center-biased checking point pattern for fast block motion estimation is proposed in this paper. A halfway-stop technique is employed in the new algorithm with searching steps of 2 to 4 and the total number of checking points is varied from 17 to 27. Simulation results show that the proposed 4SS performs better than the well-known three-step search and has similar performance to the new three-step search (N3SS) in terms of motion compensation errors. In addition, the 4SS also reduces the worst-case computational requirement from 33 to 27 search points and the average computational requirement from 21 to 19 search points, as compared with N3SS.

A novel cross-diamond search algorithm for fast block motion estimation

Abstract: In block motion estimation, search patterns with different shapes or sizes and the center-biased characteristics of motion-vector distribution have a large impact on the searching speed and quality of performance. We propose a novel algorithm using a cross-search pattern as the initial step and large/small diamond search (DS) patterns as the subsequent steps for fast block motion estimation. The initial cross-search pattern is designed to fit the cross-center-biased motion vector distribution characteristics of the real-world sequences by evaluating the nine relatively higher probable candidates located horizontally and vertically at the center of the search grid. The proposed cross-diamond search (CDS) algorithm employs the halfway-stop technique and finds small motion vectors with fewer search points than the DS algorithm while maintaining similar or even better search quality. The improvement of CDS over DS can be up to a 40% gain on speedup. Experimental results show that the CDS is much more robust, and provides faster searching speed and smaller distortions than other popular fast block-matching algorithms.

Normalized partial distortion search algorithm for block motion estimation

Abstract: Many fast block-matching algorithms reduce computations by limiting the number of checking points. They can achieve high computation reduction, but often result in relatively higher matching error compared with the full-search algorithm. A novel fast block-matching algorithm named normalized partial distortion search is proposed. The proposed algorithm reduces computations by using a halfway-stop technique in the calculation of the block distortion measure. In order to increase the probability of early rejection of non-possible candidate motion vectors, the proposed algorithm normalized the accumulated partial distortion and the current minimum distortion before comparison. Experimental results show that the proposed algorithm can maintain its mean square error performance very close to the full-search algorithm while achieving an average computation reduction of 12-13 times, with respect to the full-search algorithm.

Novel cross-diamond-hexagonal search algorithms for fast block motion estimation

Abstract: We propose two cross-diamond-hexagonal search (CDHS) algorithms, which differ from each other by their sizes of hexagonal search patterns. These algorithms basically employ two cross-shaped search patterns consecutively in the very beginning steps and switch using diamond-shaped patterns. To further reduce the checking points, two pairs of hexagonal search patterns are proposed in conjunction with candidates found located at diamond corners. Experimental results show that the proposed CDHSs perform faster than the diamond search (DS) by about 144% and the cross-diamond search (CDS) by about 73%, whereas similar prediction quality is still maintained.

A new diamond search algorithm for fast block-matching motion estimation

Abstract: Based on the study of motion vector distribution from several commonly used test image sequences, a new diamond search (DS) algorithm for fast block-matching motion estimation (BMME) is proposed in this paper. Simulation results demonstrate that the proposed DS algorithm greatly outperforms the well-known three-step search (TSS) algorithm. Compared with the new three-step search (NTSS) algorithm, the DS algorithm achieves close performance but requires less computation by up to 22% on average. Experimental results also show that the DS algorithm is better than the four-step search (4SS) and block-based gradient descent search (BBGDS), in terms of mean-square error performance and required number of search points.

Most apparent distortion: full-reference image quality assessment and the role of strategy

Abstract: The mainstream approach to image quality assessment has centered around accurately modeling the single most relevant strategy employed by the human visual system (HVS) when judging image quality (e.g., detecting visible differences, and extracting image structure/information). In this work, we suggest that a single strategy may not be sufficient; rather, we advocate that the HVS uses multiple strategies to determine image quality. For images containing near-threshold distortions, the image is most apparent, and thus the HVS attempts to look past the image and look for the distortions (a detection-based strategy). For images containing clearly visible distortions, the distortions are most apparent, and thus the HVS attempts to look past the distortion and look for the image's subject matter (an appearance-based strategy). Here, we present a quality assessment method [most apparent distortion (MAD)], which attempts to explicitly model these two separate strategies. Local luminance and contrast masking are used to estimate detection-based perceived distortion in high-quality images, whereas changes in the local statistics of spatial-frequency components are used to estimate appearance-based perceived distortion in low-quality images. We show that a combination of these two measures can perform well in predicting subjective ratings of image quality.

Quantization

Abstract: Quantization is a process that maps a continous or discrete set of values into approximations that belong to a smaller set. Quantization is a lossy: some information about the original data is lost in the process. The key to a successful quantization is therefore the selection of an error criterion – such as entropy and signal-to-noise ratio – and the development of optimal quantizers for this criterion.