Low Power Video Decoding with Adaptive Granularity in Temporal Scalability

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概要

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• This paper proposes a low power video decoding with adaptive granularity in temporal scalability. This proposal can be applied to reduce the computational complexity of H.264/AVC decoder with acceptable loss of the video quality, and make the single layer bit stream sources much more flexible for various terminal devices. Proposed low power decoding process consists of four proposed algorithms, the reference frame index decision algorithm, motion vector composition algorithm, block-partition decision algorithm and the adaptive selecting algorithm for skipped frames. The experiment results show that the reduction rate of the decoding time decreases when the number of skipped frames increases, and the loss of the video quality increases at the same time. The PSNR loss in the B frame skipping is much smaller than the PSNR loss in the P frame skipping. In the fixed frame skipping cases, the 2/3 P frame is skipped with 60% decoding time reduction and 2.73 dB average PSNR loss in the all filling comparison or 1.59 dB average PSNR loss in the corresponding comparison. Analyzing the relation between motion vector information and the video quality loss of the corresponding frames in probability shows that the proposed adaptive skipping scheme reduces quality loss by skipping the frames with slight movements and keeping the frames with strong movements. Based on the adaptive skipping scheme, the average PSNR is improved 0.68 dB in the all filling comparison or 0.60 dB in the corresponding comparison compared with the fixed frame skipping scheme with almost the same reduction rate of the decoding time.

• 一般社団法人 画像電子学会の論文

一般社団法人 画像電子学会 | 論文

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