JND-based Perceptual Rate Distortion Optimization for AV1 Encoder

Abstract

AV1 is the next-generation open video coding format, and it can achieve significant coding efficiency with novel coding tools. It supports Lagrangian rate distortion optimization (RDO) method to optimize the coding performance. However, the distortion and the Lagrangian multiplier used in RDO ignore the characteristics of human visual system (HVS), which leads to insufficiency for perceptual video coding. To solve this problem, a perceptual RDO scheme based on the Just Noticeable Distortion (JND) threshold of HVS is proposed. The JND for each pixel is first measured according to three perceptual features: luminance adaptation, masking effects and structure sensitivity. Based on the observation that the regions with smaller distortion visibility thresholds are more sensitive to HVS, a JND-based Lagrangian multiplier is derived to adaptively adjust the rate-distortion (RD) performance for each coding block. Experiments demonstrate that the proposed method can achieve an average SSIM-based -3.93% BD-Rate saving compared with the original AV1 encoder, which effectively improve the coding performance.

Publication
2019 Picture Coding Symposium (PCS)
Chen Zhu
Chen Zhu
PhD Student

I’m a Research PHD candidate at SJTU Media Lab. My research interest is video coding optimization, under the direction of Prof. Li Song.

Li Song
Li Song
Professor, IEEE Senior Member

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