Just Noticeable Distortion Based Perceptually Lossless Intra Coding
Xuelin Shen, Xinfeng Zhang, Shiqi Wang, Sam Kwong, Guopu Zhu
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Perceptual video coding plays a very important role in video codec optimization aiming at removing the perceptual redundancies in video content. In this paper, a just noticeable distortion (JND) guided perceptually lossless coding framework is proposed for Versatile Video Coding (VVC) intra coding. Within this framework, a pattern-based pixel wise JND model is employed to guide the distortion distribution, and subsequently the most appropriate quantization parameter is chosen for each Coding Tree Unit (CTU). The content adaptive Laplacian distribution based D-Q model based on a two pass coding framework is established to derive the most proper QP that satisfies the perceptually lossless coding criteria. The whole framework is integrated into the H.266/VVC intra coding framework. Experimental results demonstrate that the proposed scheme can achieve high accuracy prediction and efficient perceptually lossless intra coding, leading to around 10% bitrate savings comparing with the frame level QP derivation scheme.