Browsing by Author "Tsai, Jang-Jer"

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A genetic rhombus pattern search for block motion estimation
(2007)
Tsai, Jang-Jer
;
Hang, Hsueh-Ming
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
Pattern-based block motion estimation (PBME) is one of the most effective yet computational intensive tools in digital video coding standards. Many PBME algorithms have been proposed but few papers have investigated in depth the fundamental characteristics of various PBME algorithms. Why one search algorithm outperforms the others? In this paper, we propose a systematic approach to examine this problem. The minimal numbers of search points achievable by a PBME algorithm form a discrete function in the search area. We analyze this so-called weighting function and suggest an ideal target. Then, we design a genetic rhombus pattern search (GRPS) to match this ideal weighting function. Simulations show that comparing to the other popular search algorithms, GRPS reduces the average search points for more than 20% while it maintains a similar level of coded image PSNR quality.
A TRIANGULAR-WARPING BASED VIEW SYNTHESIS SCHEME WITH ENHANCED ARTIFACT REDUCTION FOR FTV
(2011)
Li, Chao-Hsuan
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Tsai, Jang-Jer
;
Hang, Hsueh-Ming
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
View synthesis is one key enabling technology for free-viewpoint television (FTV). It uses multiple image frames and depth maps to generate the intermediate view at nearly any arbitrary viewpoint. In our proposed 2-stage algorithm, we adopt triangular warping with a new feature point extraction scheme for the first texture mapping stage. The new feature point extraction scheme uses the correlation between the image luminance gradients and the depth map. In the second stage, we use the median filtering and the multi-band blending technique to reduce the artifacts on the image object boundaries caused by the discontinuity and the imperfection of depth maps. Experimental results show that our proposed method produces better visual quality in comparison with a latest view synthesis algorithm.
Modeling of Pattern-Based Block Motion Estimation and Its Application
(2009-01-01)
Tsai, Jang-Jer
;
Hang, Hsueh-Ming
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
Pattern-based block motion estimation (PBME) is one of the most widely adopted compression tools in the contemporary video coding systems. However, despite that many researches have studied PBME, few have yet attempted to construct an analytical model that can explain the underneath principle and mechanism of various PBME algorithms. In this paper, we propose a statistical PBME model that consists of two components: 1) a statistical probability distribution for motion vectors and 2) the minimal number of search points (so-called weighting function) achieved by a search algorithm. We first verify the accuracy of the proposed model by checking the experimental data. Then, an application example using this model is shown. Starting from an ideal weighting function, we devise a novel genetic rhombus pattern search (GRIPS) to match the design target. Simulations show that, comparing to the other popular search algorithms, GRIPS reduces the average search points for more than 20% and, in the meanwhile, it maintains a similar level of coded image quality.
On adaptive pattern selection for block motion estimation algorithms
(2007)
Tsai, Jang-Jer
;
Hang, Hseuh-Ming
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
Pattern-based block motion estimation (PBME) algorithm has been widely adopted in digital video coding systems. Due to the large characteristics variations among video sequences, adaptive PBME algorithms that switch search patterns have been proposed. However, most adaptive search algorithms are heuristically designed based on the experimental data. In this paper, we like to construct an analytical model and explore the problem systematically. Also, we propose an adaptive genetic pattern search algorithm (AGPS). Simulations show that the proposed AGPS in average outperforms the existing popular search algorithms quite significantly in speed, while the peak signal noise ration (PSNR) quality is maintained at the same level.
ON MODELING GENETIC PATTERN SEARCH FOR BLOCK MOTION ESTIMATION
(2008)
Tsai, Jang-Jer
;
Hang, Hsueh-Ming
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
Pattern search algorithms, such as diamond search, hexagonal search and their variations, have been widely adopted by the block matching motion estimations in the modem video encoding systems. Recently we propose a weighting function (WF) to model the number of search points of a pattern search. Yet, WF fails to properly describe the behavior of the genetic pattern search algorithms due to some over-simplifications in their models. Therefore, we propose a refined weighting function (RWF) to more accurately describe both genetic and non-genetic pattern searches. In addition, we propose a new search algorithm, namely, the momentum directed genetic rhombus pattern search (MD-GRPS). It can accelerate the previous genetic rhombus pattern search by 8% on the average and this concept can be applied to the other genetic pattern searches.
On the Design of Pattern-Based Block Motion Estimation Algorithms
(2010-01-01)
Tsai, Jang-Jer
;
Hang, Hsueh-Ming
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
Pattern-based block motion estimation (PBME) is a critical element in the contemporary video coding system because it typically dominates the coding efficiency and the computing power. Therefore, many proposals have been suggested to reduce its computational complexity, but most of them are devised based on experimental data or heuristic ideas. In this letter, we look into every component of a typical PBME algorithm and fine tune the major components systematically to achieve the optimal or nearly optimal results. Our methodology is developed based on our proposed analytical model together with statistical tools. First, we use the analytic model to analyze and design effective genetic-algorithm-based search patterns. Moreover, we propose an adaptive switching strategy that dynamically switches between two search patterns. Second, we extend our PBME model to evaluate the efficiency of starting (initial search) points. A near optimal set of starting points is progressively identified. Last, we study the early termination threshold technique and suggest a metric in selecting an effective threshold. An accurate threshold mechanism is thus constructed. Combining all these techniques, we develop a PBME algorithm that outperforms most popular algorithms.