Browsing by Author "Wu, Chung-Hao"

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Analytical Mode-Dependent Rate and Distortion Models for H.264/SVC Coarse Grain Scalability
(2012)
Wu, Chung-Hao
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Tseng, Yu-Chen
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Peng, Wen-Hsiao
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
This paper presents analytical rate and distortion models to estimate the rate-distortion (R-D) behavior of different coding modes for H.264/SVC coarse grain scalability (CGS) by jointly considering the effects of quantization, motion-compensated prediction, and motion partitioning structures. Based on the forward channel model and two statistical signal models, we first derive a pair of mode-dependent rate and distortion models for coding modes in base layer. Then these models are extended to account for the coding modes with inter-layer residual prediction in enhancement layer, especially for CGS. Preliminary results show that the proposed rate and distortion models for both layers have the potentiality to estimate the R-D behavior of real data.
BI-PREDICTION COMBINING TEMPLATE AND BLOCK MOTION COMPENSATIONS
(2011)
Lee, Chung-Lin
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Chen, Chun-Chi
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Chen, Yi-Wen
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Wu, Mu-Hsuan
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Wu, Chung-Hao
This paper introduces a bi-prediction scheme with only a motion overhead as for unidirectional prediction. It combines motion vectors found by template and block matchings with the overlapped block motion compensation (OBMC). An optimal window function is designed based on a model-based framework. Additionally, the concept of adaptive motion merging is incorporated to enable a template-matching-free implementation. Three algorithms featuring different performance and complexity trade-offs are implemented using the TMuC-0.9_HM software and tested with the common test conditions. Relative to the anchor, the best of them achieves an average BD-rate saving of 2.2%, with a minimum of 0.2% and a maximum of 4.7%.
Budgeted Algorithm for Linearized Confidence-Weighted Learning
(2019-01-01)
Lin, Yu-Shiou
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Wu, Chung-Hao
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Lu, Henry Horng-Shing
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交大名義發表
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統計學研究所
This paper presents a novel algorithm for performing linearized confidence-weighted (LCW) learning on a fixed budget. LCW learning has been applied to solve online classification problems in recent years. To make better classification performance, it is common to combine with kernel functions through the kernel trick. However, the trick makes the LCW learning vulnerable to the curse of kernelization that causes unlimited growth in memory usage and run-time. To address this issue, we first re-interpret the LCW learning by using a resource perspective deeming every instance as a potential resource to exploit. Based on the perspective, we then propose a budgeted algorithm that approximates the LCW learning under a finite constraint on the number of available resources. The proposed algorithm enjoys finite complexities of time and space and thus is able to break the curse. Experiments on several open datasets show that the proposed algorithm approximates the LCW learning well and is competitive to state-of-the-art budgeted algorithms.
ON THE ANALYSIS AND DESIGN OF MOTION SAMPLING STRUCTURE FOR ADVANCED MOTION-COMPENSATED PREDICTION
(2010)
Tseng, Yu-Chen
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Wu, Chung-Hao
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Chen, Yi-Wen
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Wang, Tse-Wei
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Peng, Wen-Hsiao
This paper addresses the problem of improving motion sampling efficiency for motion-compensated prediction (MCP). We provide a theoretical framework for analyzing the effect of motion sampling structure on MCP efficiency. It is shown that the sampling grid induced by the quadtree partition in H.264/AVC is suboptimal. To improve sampling efficiency, we propose a new pattern, which provides sampling points at both the center and the top-left corner of a macroblcok. When contrasted with conventional NxN/2 block partition, the proposed scheme performs consistently and significantly better in subjective and objective quality.
Online Multiclass Passive-Aggressive Learning on a Fixed Budget
(2017-01-01)
Wu, Chung-Hao
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Hsi, Wei-Chen
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Lu, Henry Horng-Shing
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Hang, Hsueh-Ming
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統計學研究所
This paper presents a budgetary learning algorithm for online multiclass classification. Based on the multiclass passive-aggressive learning with kernels, we introduce a dual perspective that gives rise to the proposed budgetary algorithm. Basically, the proposed algorithm limits the amount of data in use and fully exploits the available data on hand through optimization. The algorithm has both constant time and space complexities and thus can avoid the curse of kernelization. Experimental results with open datasets show that the proposed budgetary algorithm is competitive with state-of-the-art algorithms.