JND-Based Perceptual Video Coding for 4:4:4 Screen Content Data in HEVC

The JCT-VC standardized Screen Content Coding (SCC) extension in the HEVC HM RExt + SCM reference codec offers an impressive coding efficiency performance when compared with HM RExt alone; however, it is not significantly perceptually optimized. For instance, it does not include advanced HVS-based perceptual coding methods, such as JND-based spatiotemporal masking schemes. In this paper, we propose a novel JND-based perceptual video coding technique for HM RExt + SCM. The proposed method is designed to further improve the compression performance of HM RExt + SCM when applied to YCbCr 4:4:4 SC video data. In the proposed technique, luminance masking and chrominance masking are exploited to perceptually adjust the Quantization Step Size (QStep) at the Coding Block (CB) level. Compared with HM RExt 16.10 + SCM 8.0, the proposed method considerably reduces bitrates (Kbps), with a maximum reduction of 48.3%. In addition to this, the subjective evaluations reveal that SC-PAQ achieves visually lossless coding at very low bitrates.Comment: Preprint: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018

Perceptual Video Coding for Machines via Satisfied Machine Ratio Modeling

Video Coding for Machines (VCM) aims to compress visual signals for machine analysis. However, existing methods only consider a few machines, neglecting the majority. Moreover, the machine perceptual characteristics are not effectively leveraged, leading to suboptimal compression efficiency. In this paper, we introduce Satisfied Machine Ratio (SMR) to address these issues. SMR statistically measures the quality of compressed images and videos for machines by aggregating satisfaction scores from them. Each score is calculated based on the difference in machine perceptions between original and compressed images. Targeting image classification and object detection tasks, we build two representative machine libraries for SMR annotation and construct a large-scale SMR dataset to facilitate SMR studies. We then propose an SMR prediction model based on the correlation between deep features differences and SMR. Furthermore, we introduce an auxiliary task to increase the prediction accuracy by predicting the SMR difference between two images in different quality levels. Extensive experiments demonstrate that using the SMR models significantly improves compression performance for VCM, and the SMR models generalize well to unseen machines, traditional and neural codecs, and datasets. In summary, SMR enables perceptual coding for machines and advances VCM from specificity to generality. Code is available at \url{https://github.com/ywwynm/SMR}

Quality-Oriented Perceptual HEVC Based on the Spatiotemporal Saliency Detection Model

Perceptual video coding (PVC) can provide a lower bitrate with the same visual quality compared with traditional H.265/high efficiency video coding (HEVC). In this work, a novel H.265/HEVC-compliant PVC framework is proposed based on the video saliency model. Firstly, both an effective and efficient spatiotemporal saliency model is used to generate a video saliency map. Secondly, a perceptual coding scheme is developed based on the saliency map. A saliency-based quantization control algorithm is proposed to reduce the bitrate. Finally, the simulation results demonstrate that the proposed perceptual coding scheme shows its superiority in objective and subjective tests, achieving up to a 9.46% bitrate reduction with negligible subjective and objective quality loss. The advantage of the proposed method is the high quality adapted for a high-definition video application

Encoding in the Dark Grand Challenge:An Overview

A big part of the video content we consume from video providers consists of genres featuring low-light aesthetics. Low light sequences have special characteristics, such as spatio-temporal varying acquisition noise and light flickering, that make the encoding process challenging. To deal with the spatio-temporal incoherent noise, higher bitrates are used to achieve high objective quality. Additionally, the quality assessment metrics and methods have not been designed, trained or tested for this type of content. This has inspired us to trigger research in that area and propose a Grand Challenge on encoding low-light video sequences. In this paper, we present an overview of the proposed challenge, and test state-of-the-art methods that will be part of the benchmark methods at the stage of the participants' deliverable assessment. From this exploration, our results show that VVC already achieves a high performance compared to simply denoising the video source prior to encoding. Moreover, the quality of the video streams can be further improved by employing a post-processing image enhancement method