Piecewise mapping in HEVC lossless intra-prediction coding

The lossless intra-prediction coding modality of the High Efficiency Video Coding (HEVC) standard provides high coding performance while following frame-by-frame basis access to the coded data. This is of interest in many professional applications such as medical imaging, automotive vision and digital preservation in libraries and archives. Various improvements to lossless intra-prediction coding have been proposed recently, most of them based on sample-wise prediction using Differential Pulse Code Modulation (DPCM). Other recent proposals aim at further reducing the energy of intra-predicted residual blocks. However, the energy reduction achieved is frequently minimal due to the difficulty of correctly predicting the sign and magnitude of residual values. In this paper, we pursue a novel approach to this energy-reduction problem using piecewise mapping (pwm) functions. Specifically, we analyze the range of values in residual blocks and apply accordingly a pwm function to map specific residual values to unique lower values. We encode appropriate parameters associated with the pwm functions at the encoder, so that the corresponding inverse pwm functions at the decoder can map values back to the same residual values. These residual values are then used to reconstruct the original signal. This mapping is, therefore, reversible and introduces no losses. We evaluate the pwm functions on 4×4 residual blocks computed after DPCM-based prediction for lossless coding of a variety of camera-captured and screen content sequences. Evaluation results show that the pwm functions can attain maximum bit-rate reductions of 5.54% and 28.33% for screen content material compared to DPCM-based and block-wise intra-prediction, respectively. Compared to IntraBlock Copy, piecewise mapping can attain maximum bit-rate reductions of 11.48% for camera-captured material

Weighted bi-prediction for light field image coding

Light field imaging based on a single-tier camera equipped with a microlens array – also known as integral, holoscopic, and plenoptic imaging – has currently risen up as a practical and prospective approach for future visual applications and services. However, successfully deploying actual light field imaging applications and services will require developing adequate coding solutions to efficiently handle the massive amount of data involved in these systems. In this context, self-similarity compensated prediction is a non-local spatial prediction scheme based on block matching that has been shown to achieve high efficiency for light field image coding based on the High Efficiency Video Coding (HEVC) standard. As previously shown by the authors, this is possible by simply averaging two predictor blocks that are jointly estimated from a causal search window in the current frame itself, referred to as self-similarity bi-prediction. However, theoretical analyses for motion compensated bi-prediction have suggested that it is still possible to achieve further rate-distortion performance improvements by adaptively estimating the weighting coefficients of the two predictor blocks. Therefore, this paper presents a comprehensive study of the rate-distortion performance for HEVC-based light field image coding when using different sets of weighting coefficients for self-similarity bi-prediction. Experimental results demonstrate that it is possible to extend the previous theoretical conclusions to light field image coding and show that the proposed adaptive weighting coefficient selection leads to up to 5 % of bit savings compared to the previous self-similarity bi-prediction scheme.info:eu-repo/semantics/acceptedVersio

Spatiotemporal adaptive quantization for the perceptual video coding of RGB 4:4:4 data

Due to the spectral sensitivity phenomenon of the Human Visual System (HVS), the color channels of raw RGB 4:4:4 sequences contain significant psychovisual redundancies; these redundancies can be perceptually quantized. The default quantization systems in the HEVC standard are known as Uniform Reconstruction Quantization (URQ) and Rate Distortion Optimized Quantization (RDOQ); URQ and RDOQ are not perceptually optimized for the coding of RGB 4:4:4 video data. In this paper, we propose a novel spatiotemporal perceptual quantization technique named SPAQ. With application for RGB 4:4:4 video data, SPAQ exploits HVS spectral sensitivity-related color masking in addition to spatial masking and temporal masking; SPAQ operates at the Coding Block (CB) level and the Prediction Unit (PU) level. The proposed technique perceptually adjusts the Quantization Step Size (QStep) at the CB level if high variance spatial data in G, B and R CBs is detected and also if high motion vector magnitudes in PUs are detected. Compared with anchor 1 (HEVC HM 16.17 RExt), SPAQ considerably reduces bitrates with a maximum reduction of approximately 80%. The Mean Opinion Score (MOS) in the subjective evaluations, in addition to the SSIM scores, show that SPAQ successfully achieves perceptually lossless compression compared with anchors

A comprehensive video codec comparison

In this paper, we compare the video codecs AV1 (version 1.0.0-2242 from August 2019), HEVC (HM and x265), AVC (x264), the exploration software JEM which is based on HEVC, and the VVC (successor of HEVC) test model VTM (version 4.0 from February 2019) under two fair and balanced configurations: All Intra for the assessment of intra coding and Maximum Coding Efficiency with all codecs being tuned for their best coding efficiency settings. VTM achieves the highest coding efficiency in both configurations, followed by JEM and AV1. The worst coding efficiency is achieved by x264 and x265, even in the placebo preset for highest coding efficiency. AV1 gained a lot in terms of coding efficiency compared to previous versions and now outperforms HM by 24% BD-Rate gains. VTM gains 5% over AV1 in terms of BD-Rates. By reporting separate numbers for JVET and AOM test sequences, it is ensured that no bias in the test sequences exists. When comparing only intra coding tools, it is observed that the complexity increases exponentially for linearly increasing coding efficiency

Joint exploration model based light field image coding: A comparative study

© 2017 IEEE. The recent light field imaging technology has been attracting a lot of interests due to its potential applications in a large number of areas including Virtual Reality, Augmented Reality (VR/AR), Teleconferencing, and E-learning. Light Field (LF) data is able to provide rich visual information such as scene rendering with changes in depth of field, viewpoint, and focal length. However, Light Field data usually associates to a critical problem - the massive data. Therefore, compressing LF data is one of the main challenges in LF research. In this context, we present in this paper a comparative study for compressing LF data with not only the widely used image/video coding standards, such as JPEG-2000, H.264/AVC, HEVC and Google/VP9 but also with the most recent image/video coding solution, the Joint Exploration Model. In addition, this paper also proposes a LF image coding flow, which can be used as a benchmark for future LF compression evaluation. Finally, the compression efficiency of these coding solutions is thoroughly compared throughout a rich set of test conditions

CANF-VC++: Enhancing Conditional Augmented Normalizing Flows for Video Compression with Advanced Techniques

Video has become the predominant medium for information dissemination, driving the need for efficient video codecs. Recent advancements in learned video compression have shown promising results, surpassing traditional codecs in terms of coding efficiency. However, challenges remain in integrating fragmented techniques and incorporating new tools into existing codecs. In this paper, we comprehensively review the state-of-the-art CANF-VC codec and propose CANF-VC++, an enhanced version that addresses these challenges. We systematically explore architecture design, reference frame type, training procedure, and entropy coding efficiency, leading to substantial coding improvements. CANF-VC++ achieves significant Bj{\o}ntegaard-Delta rate savings on conventional datasets UVG, HEVC Class B and MCL-JCV, outperforming the baseline CANF-VC and even the H.266 reference software VTM. Our work demonstrates the potential of integrating advancements in video compression and serves as inspiration for future research in the field

HEVC-based 3D holoscopic video coding using self-similarity compensated prediction

Holoscopic imaging, also known as integral, light field, and plenoptic imaging, is an appealing technology for glassless 3D video systems, which has recently emerged as a prospective candidate for future image and video applications, such as 3D television. However, to successfully introduce 3D holoscopic video applications into the market, adequate coding tools that can efficiently handle 3D holoscopic video are necessary. In this context, this paper discusses the requirements and challenges for 3D holoscopic video coding, and presents an efficient 3D holoscopic coding scheme based on High Efficiency Video Coding (HEVC). The proposed 3D holoscopic codec makes use of the self-similarity (SS) compensated prediction concept to efficiently explore the inherent correlation of the 3D holoscopic content in Intra- and Inter-coded frames, as well as a novel vector prediction scheme to take advantage of the peculiar characteristics of the SS prediction data. Extensive experiments were conducted, and have shown that the proposed solution is able to outperform HEVC as well as other coding solutions proposed in the literature. Moreover, a consistently better performance is also observed for a set of different quality metrics proposed in the literature for 3D holoscopic content, as well as for the visual quality of views synthesized from decompressed 3D holoscopic content.info:eu-repo/semantics/submittedVersio

Weighted Combination of Sample Based and Block Based Intra Prediction in Video Coding

The latest standard within video compression, HEVC/H.265, was released during 2013 and provides a significant improvement from its predecessor AVC/H.264. However, with a constantly increasing demand for high denition video and streaming of large video files, there are still improvements that can be done. Difficult content in video sequences, for example smoke, leaves and water that moves irregularly, is being hard to predict and can be troublesome at the prediction stage in the video compression. In this thesis, carried out at Ericsson in Stockholm, the combination of sample based intra prediction (SBIP) and block based intra prediction (BBIP) is tested to see if it could improve the prediction of video sequences containing difficult content, here focusing on water. The combined methods are compared to HEVC intra prediction. All implementations have been done in Matlab. The results show that a combination reduces the Mean Squared Error (MSE) as well as could improve the Visual Information Fidelity (VIF) and the mean Structural Similarity (MSSIM). Moreover the visual quality was improved by more details and less blocking artefacts