使用深度不连续预测和环内边界重建滤波的深度编码

R. Farrugia, Maverick Hili
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引用次数: 0

摘要

本文提出了一种深度编码策略,利用K均值聚类将深度图像序列分割成K个聚类。所得到的聚类被无损压缩并作为补充增强信息传输,以帮助解码器预测包含深度不连续的宏块。该方法进一步采用环内边界重建滤波器来减少边缘处的畸变。该算法集成在H.264/AVC和H.264/MVC视频编码标准中。仿真结果表明,所提出的方案优于当前深度编码方案,其中呈现的峰值信噪比(PSNR)增益在0.1 dB和0.5 dB之间。
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Depth coding using depth discontinuity prediction and in-loop boundary reconstruction filtering
This paper presents a depth coding strategy that employs K-means clustering to segment the sequence of depth images into K clusters. The resulting clusters are losslessly compressed and transmitted as supplemental enhancement information to aid the decoder in predicting macroblocks containing depth discontinuities. This method further employs an in-loop boundary reconstruction filter to reduce distortions at the edges. The proposed algorithm was integrated within both H.264/AVC and H.264/MVC video coding standards. Simulation results demonstrate that the proposed scheme outperforms the state of the art depth coding schemes, where rendered Peak Signal to Noise Ratio (PSNR) gains between 0.1 dB and 0.5 dB were observed.
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