In this paper, we propose a perceptual-based distributed video coding (DVC) technique. Unlike traditional video codecs, DVC applies video prediction process at the decoder side using previously received frames. The pr...
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In this paper, we propose a perceptual-based distributed video coding (DVC) technique. Unlike traditional video codecs, DVC applies video prediction process at the decoder side using previously received frames. The predicted video frames (i.e., side information) contain prediction errors. The encoder then transmits error-correcting parity bits to the decoder to reconstruct the video frames from side information. However, channel codes based on i.i.d. noise models are not always efficient in correcting video prediction errors. In addition, some of the prediction errors do not cause perceptible visual distortions. From perceptualcoding point of view, there is no need to correct such errors. This paper proposes a scheme for the decoder to perform perceptual quality analysis on the predicted side information. The decoder only requests parity bits to correct visually sensitive errors. More importantly, with the proposed technique, key frames can be encoded at higher rates while still maintaining consistent visual quality across the video sequence. As a result, even the objective PSNR measure of the decoded video sequence will increase too. Experimental results show that the proposed technique improves the R-D performance of a transform domain DVC codec both subjectively and objectively. Comparisons with a well-known DVC codec show that the proposed perceptual-based DVC coding scheme is very promising for distributed video coding framework. (C) 2012 Elsevier Inc. All rights reserved.
In this paper, we present a method for performing rate-distortion optimization (RDO) using a perceptual visual quality metric, the structural similarity index (SSIM), as the target of optimization. Rate-distortion opt...
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In this paper, we present a method for performing rate-distortion optimization (RDO) using a perceptual visual quality metric, the structural similarity index (SSIM), as the target of optimization. Rate-distortion optimization is widely used in modern video codecs to make various encoder decisions to optimize the rate-distortion tradeoff. Typically, the distortion measure used is either sum-of-square error or sum-of-absolute distance, both of which are convenient when used in the RDO framework but not always reflective of a perceptual visual quality. We show that SSIM can be used as the distortion metric in the RDO framework in a simple, yet effective, manner by scaling the Lagrange multiplier used in RDO based on the local variance in that region. The experimental results on the H.264/AVC reference software show that compared to traditional RDO approaches, for the same SSIM score, the proposed approach can achieve an average rate reduction of about 9% and 14% for random access and low-delay encoding configurations. At the same time, there is no significant change in the encoding runtime.
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