Perceptual ultra-high definition video coding based on adaptive just noticeable distortion model

Abstract

Ultra-high definition (UHD) video is gradually applied into many fields. The huge amount of data in UHD video limits the realization of these applications. Perception video coding can exploit the visual redundancy, and improve the compression performance. So far, the perpetual video coding based on just noticeable distortion (JND) model cannot fully exploit the perceptual redundancy of ultra-high definition (UHD) video/image in 10-bit depth. Thus, an adaptive JND model based on perceptual noise Bayesian network (PNBN) is firstly established for UHD video/image. Then, combined perceptual noise channel encoding/decoding with human visual characteristics, hidden side information of image is dynamically estimated by PNBN. Thirdly, the adaptive JND model is considered both luminance masking effect and texture complexity, which can adaptively exploit redundancy in different texture regions of image. Finally, an effective perceptual video coding (PVC) scheme is proposed for versatile video coding. A novel distortion compensation factor is appropriately designed for rate-distortion optimization. Experimental results show that the proposed PVC scheme achieves significant bitrate reduction with better subjective video quality than the state-of-the-art PVC schemes.