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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Hang, Hsueh-MingSubject: search.filters.subject.modeling

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    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.
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    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.