Fast HEVC Intramode Decision Based on Hybrid Cost Ranking

To improve rate-distortion (R-D) performance, high efficiency video coding (HEVC) increases the intraprediction modes with heavy computational load, and thus the intracoding optimization is highly demanded for real-time applications. According to the conditional probabilities of most probable modes and the correlation of potential candidate subsets, this paper proposes a fast HEVC intramode decision scheme based on the hybrid cost ranking which includes both Hadamard cost and rate-distortion cost. The proposed scheme utilizes the coded results of the modified rough mode decision and the neighboring prediction units so as to obtain a potential candidate subset and then conditionally selects the optimal mode through early likelihood decision and hybrid cost ranking. By the experiment-driven methodology, the proposed scheme implements the early termination if the best mode from the candidate subset is equal to one or two neighboring intramodes. The experimental results demonstrate that the proposed scheme averagely provides about 23.7% encoding speedup with just 0.82% BD-rate loss in comparison with default fast intramode decision in HM16.0. Compared to other fast intramode decision schemes, the proposed scheme also significantly reduces intracoding time while maintaining similar R-D performance for the all-intraconfiguration in HM16.0 Main profile

Alogorithms for fast implementation of high efficiency video coding

Recently, there is higher demand for video content in multimedia communication, which leads to increased requirements for storage and bandwidth posed to internet service providers. Due to this, it became necessary for the telecommunication standardization sector of the International Telecommunication Union (ITU-T) to launch a new video compression standard that would address the twin challenges of lowering both digital file sizes in storage media and transmission bandwidths in networks. The High Efficiency Video Compression (HEVC) also known as H.265 standard was launched in November 2013 to address these challenges. This new standard was able to cut down, by 50%, on existing media file sizes and bandwidths but its computational complexity leads to about 400% delay in HEVC video encoding. This study proposes a solution to the above problem based on three key areas of the HEVC. Firstly, two fast motion estimation algorithms are proposed based on triangle and pentagon structures to implement motion estimation and compensation in a shorter time. Secondly, an enhanced and optimized inter-prediction mode selection is proposed. Thirdly, an enhanced intra-prediction mode scheme with reduced latency is suggested. Based on the test model of the HEVC reference software, each individual algorithm manages to reduce the encoding time across all video classes by an average of 20-30%, with a best reduction of 70%, at a negligible loss in coding efficiency and video quality degradation. In practice, these algorithms would be able to enhance the performance of the HEVC compression standard, and enable higher resolution and higher frame rate video encoding as compared to the stateof- the-art technique

Machine Learning-Based Quality-Aware Power and Thermal Management of Multistream HEVC Encoding on Multicore Servers

The emergence of video streaming applications, together with the users’ demand for high-resolution contents, has led to the development of new video coding standards, such as High Efficiency Video Coding (HEVC). HEVC provides high efficiency at the cost of increased complexity. This higher computational burden results in increased power consumption in current multicore servers. To tackle this challenge, algorithmic optimizations need to be accompanied by content-aware application-level strategies, able to reduce power while meeting compression and quality requirements. In this paper, we propose a machine learning-based power and thermal management approach that dynamically learns and selects the best encoding configuration and operating frequency for each of the videos running on multicore servers, by using information from frame compression, quality, encoding time, power, and temperature. In addition, we present a resolution-aware video assignment and migration strategy that reduces the peak and average temperature of the chip while maintaining the desirable encoding time. We implemented our approach in an enterprise multicore server and evaluated it under several common scenarios for video providers. On average, compared to a state-of-the-art technique, for the most realistic scenario, our approach improves BD-PSNR and BD-rate by 0.54 dB, and 8%, respectively, and reduces the encoding time, power consumption, and average temperature by 15.3%, 13%, and 10%, respectively. Moreover, our proposed approach increases BD-PSNR and BD-rate compared to the HEVC Test Model (HM), by 1.19 dB and 24%, respectively, without any encoding time degradation, when power and temperature constraints are relaxed