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In this paper, a new Complexity Scalable Motion Estimation (CSME) method is proposed which can perform Motion Estimation adaptively under different computation or power budgets while keeping high coding performance. We first propose to use a new class-based method to measure the Macroblock (MB) importance. Based on the new MB importance measure, a complete CSME framework is then proposed. The proposed...
In this paper, a new computation-control motion estimation (CCME) method is proposed which can perform motion estimation (ME) adaptively under different computation or power budgets while keeping high coding performance. We first propose a new class-based method to measure the macroblock (MB) importance where MBs are classified into different classes and their importance is measured by combining their...
In this paper, a new complexity-scalable computation control method is proposed which can perform motion estimation (ME) adaptively under different computation or power budgets while keeping high coding performance. We first propose a new class-based method to measure the macroblock (MB) importance where MBs are classified into different classes and their importance is measured by combining their...
Motion Estimation (ME) is one of the most time-consuming parts in video coding. It is always desirable to develop fast ME algorithms to reduce the ME complexity. In this paper, a new early termination method is proposed for fast motion estimation. The proposed method first classifies each macroblock into one of three classes based on the estimation of the possible matching cost improvement from future...
We formalize data scaling classification (DSC) as a technique to trade the accuracy of classification with the network transmission load in stream analysis frameworks. We apply the proposed data scaling approaches to ECG classification in remote health monitoring systems. Experimental results show satisfactory resource savings for small amounts of utility degradation (e.g., 33% of bandwidth saving...
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