Efficient HEVC Encoding to Meet Bitrate and PSNR Requirements Using Parametric Modeling

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.