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

Low complexity in-loop perceptual video coding

The tradition of broadcast video is today complemented with user generated content, as portable devices support video coding. Similarly, computing is becoming ubiquitous, where Internet of Things (IoT) incorporate heterogeneous networks to communicate with personal and/or infrastructure devices. Irrespective, the emphasises is on bandwidth and processor efficiencies, meaning increasing the signalling options in video encoding. Consequently, assessment for pixel differences applies uniform cost to be processor efficient, in contrast the Human Visual System (HVS) has non-uniform sensitivity based upon lighting, edges and textures. Existing perceptual assessments, are natively incompatible and processor demanding, making perceptual video coding (PVC) unsuitable for these environments. This research allows existing perceptual assessment at the native level using low complexity techniques, before producing new pixel-base image quality assessments (IQAs). To manage these IQAs a framework was developed and implemented in the high efficiency video coding (HEVC) encoder. This resulted in bit-redistribution, where greater bits and smaller partitioning were allocated to perceptually significant regions. Using a HEVC optimised processor the timing increase was < +4% and < +6% for video streaming and recording applications respectively, 1/3 of an existing low complexity PVC solution. Future work should be directed towards perceptual quantisation which offers the potential for perceptual coding gain

Visually lossless coding in HEVC : a high bit depth and 4:4:4 capable JND-based perceptual quantisation technique for HEVC

Due to the increasing prevalence of high bit depth and YCbCr 4:4:4 video data, it is desirable to develop a JND-based visually lossless coding technique which can account for high bit depth 4:4:4 data in addition to standard 8-bit precision chroma subsampled data. In this paper, we propose a Coding Block (CB)-level JND-based luma and chroma perceptual quantisation technique for HEVC named Pixel-PAQ. Pixel-PAQ exploits both luminance masking and chrominance masking to achieve JND-based visually lossless coding; the proposed method is compatible with high bit depth YCbCr 4:4:4 video data of any resolution. When applied to YCbCr 4:4:4 high bit depth video data, Pixel-PAQ can achieve vast bitrate reductions – of up to 75% (68.6% over four QP data points) – compared with a state-of-the-art luma-based JND method for HEVC named IDSQ. Moreover, the participants in the subjective evaluations confirm that visually lossless coding is successfully achieved by Pixel-PAQ (at a PSNR value of 28.04 dB in one test)

Review of standard traditional distortion metrics and a need for perceptual distortion metric at a (sub) macroblock level

Within a video encoder the distortion metric performs an Image Quality Assessment (IQA). However, to exploit perceptual redundancy to lower the convex hull of the Rate- Distortion (R-D) curve, a Perceptual Distortion Metric (PDM) modelling of the Human Visual System (HVS) should be used. Since block-based video encoders like H.264/AVC operate at the Sub-Macroblock (Sub-MB) level, there exists a need to produce a locally operating PDM. A locally operating PDM must meet the requirements of Standard Traditional Distortion Metrics (STDMs), in that it must satisfy the Triangle Equality Rule. Hence, this paper presents a review of STDMs of SSE, SAD and SATD against the perceptual IQA of Structural Similarity (SSIM) at the Sub-MB level. Furthermore, this paper illustrates the Universal Bounded Region (UBR) by block size that supports the triangle equality rule within the Sub-MB level, between SSIM and STDMs like SATD at the prediction stage

Methods for Improving the Tone Mapping for Backward Compatible High Dynamic Range Image and Video Coding

International audienceBackward compatibility for high dynamic range image and video compression forms one of the essential requirements in the transition phase from low dynamic range (LDR) displays to high dynamic range (HDR) displays. In a recent work [1], the problems of tone mapping and HDR video coding are originally fused together in the same mathematical framework, and an optimized solution for tone mapping is achieved in terms of the mean square error (MSE) of the logarithm of luminance values. In this paper, we improve this pioneer study in three aspects by considering its three shortcomings. First, the proposed method [1] works over the logarithms of luminance values which are not uniform with respect to Human Visual System (HVS) sensitivity. We propose to use the perceptually uniform luminance values as an alternative for the optimization of tone mapping curve. Second, the proposed method [1] does not take the quality of the resulting tone mapped images into account during the formulation in contrary to the main goal of tone mapping research. We include the LDR image quality as a constraint to the optimization problem and develop a generic methodology to compromise the trade-off between HDR and LDR image qualities for coding. Third, the proposed method [1] simply applies a low-pass filter to the generated tone curves for video frames to avoid flickering during the adaptation of the method to the video. We instead include an HVS based flickering constraint to the optimization and derive a methodology to compromise the trade-off between the rate-distortion performance and flickering distortion. The superiority of the proposed methodologies is verified with experiments on HDR images and video sequences

Content-Split Block Search Algorithm Based High Efficiency Video Coding

690-693In this paper, the video streaming generation in H.265 using novel technique based on content split block (CSB) search algorithm is presented. The proposed algorithm exploits the Inter and Intra prediction through motion estimation and compensation (IPME) encoded to use four different QPs: 22, 27, 32, and 37, during the redundancy analysis in order to improve the quality of video frame encoded. The proposed algorithm exhibits the useful property of block structure based on content-tree representation for each and every frame to IPME coded without affecting either the bit rate of video stream and perceptual quality of the video frame. The proposed Search algorithm improves the visual quality of coded video frame and reduces the blocking artefacts of video frame passed through multi-stages of H.265

Algorithms for compression of high dynamic range images and video

The recent advances in sensor and display technologies have brought upon the High Dynamic Range (HDR) imaging capability. The modern multiple exposure HDR sensors can achieve the dynamic range of 100-120 dB and LED and OLED display devices have contrast ratios of 10^5:1 to 10^6:1. Despite the above advances in technology the image/video compression algorithms and associated hardware are yet based on Standard Dynamic Range (SDR) technology, i.e. they operate within an effective dynamic range of up to 70 dB for 8 bit gamma corrected images. Further the existing infrastructure for content distribution is also designed for SDR, which creates interoperability problems with true HDR capture and display equipment. The current solutions for the above problem include tone mapping the HDR content to fit SDR. However this approach leads to image quality associated problems, when strong dynamic range compression is applied. Even though some HDR-only solutions have been proposed in literature, they are not interoperable with current SDR infrastructure and are thus typically used in closed systems. Given the above observations a research gap was identified in the need for efficient algorithms for the compression of still images and video, which are capable of storing full dynamic range and colour gamut of HDR images and at the same time backward compatible with existing SDR infrastructure. To improve the usability of SDR content it is vital that any such algorithms should accommodate different tone mapping operators, including those that are spatially non-uniform. In the course of the research presented in this thesis a novel two layer CODEC architecture is introduced for both HDR image and video coding. Further a universal and computationally efficient approximation of the tone mapping operator is developed and presented. It is shown that the use of perceptually uniform colourspaces for internal representation of pixel data enables improved compression efficiency of the algorithms. Further proposed novel approaches to the compression of metadata for the tone mapping operator is shown to improve compression performance for low bitrate video content. Multiple compression algorithms are designed, implemented and compared and quality-complexity trade-offs are identified. Finally practical aspects of implementing the developed algorithms are explored by automating the design space exploration flow and integrating the high level systems design framework with domain specific tools for synthesis and simulation of multiprocessor systems. The directions for further work are also presented

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}