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Video is extensively used in everyday applications and services by heterogeneous users. High-definition video information corresponds to high-data-rate representation. Video coding scalability has an effect on bitrate requirement and peak signal-to-noise ratio (PSNR). However, the spatial information (SI) and temporal information (TI), that are inherent properties of the video, need to be maintained for the full-high-definition videos while performing scalably encoding using high-efficiency video coding (HEVC) standard. In this paper, we have established an empirical relationship of SI, TI, PSNR, and bitrate with quantization parameter (QP). Based on this study, parametric models have been derived that achieve correlation accuracy of at least 99.18 % . An optimization algorithm has been developed to find the best QP to achieve the highest PSNR and the least bitrate while maintaining SI and TI during the scalable transcoding of the video. Furthermore, the algorithmic architecture calculates the optimized QP that meets the heterogeneous user-end specification of minimum PSNR and maximum bitrate. The proposed architecture consumes low power and energy of 147 microwatts and 0.186 picojoules, respectively, and therefore, it can be integrated in an HEVC encoder for portable HEVC-compliant consumer electronic products. [ABSTRACT FROM AUTHOR]

High Efficiency Video Coding (HEVC) is the recent video coding standard that can compress raw video at a higher compression state. The extension of HEVC, Scalable High Efficiency Video Coding (SHVC), also has the similar compression phenomenon of HEVC in addition to the implementation of multiple single-layer HEVC streams along with the interlayer reference modules, although the layer-based SHVC incurs more artifacts after compression compared to HEVC resulting with severe degradation in the video quality. To ease this, in-loop filter is used to remove artifacts in H.265 video coding standard. Although the artifacts will be more severe for multiple-layered codec SHVC compared to single-layer HEVC. With the development in deep learning, a group-normalized deep convolutional neural network (gDCNN) is proposed for SHVC in-loop filter to enhance the performance. Initially, the troubles that are met while modeling the traditional CNN that includes normalization, learning capability and the loss functions are examined. Following, on the basis of statistical analysis, the proposed gDCNN is introduced to remove the artifacts efficiently. It is achieved by a group-wise normalization approach, a feature extraction and fusion and a precise loss function. The simulation setting shows 4.2% BD-BR decrement with 0.46 dB increment in BD-PSNR. [ABSTRACT FROM AUTHOR]

Highlights: [•] The first universal intra coding for mosaic videos with arbitrary RGB-CFAs in HEVC. [•] The proposed scheme avoids the difficulty in converting irregular CFA structures. [•] No color domain transformation is involved in the proposed scheme. [•] Empirical results on ten typical RGB-CFAs demonstrate our algorithm is superior. [ABSTRACT FROM AUTHOR]