Quality and Energy-Aware HEVC Transrating Based on Machine Learning

Abstract

Video transrating has become an essential task to allow the transmission of different versions of the same video in streaming services and live applications. However, as the transrating operation comprises a decoding and an encoding step in sequence, it demands high processing time and energy consumption, which is prohibitive in large-scale systems. This paper proposes a scalable quality and time/energy-aware high-efficiency video coding (HEVC) transrating system based on decision trees. The scalable scheme operates under three different modes that employ the decision tree outcomes in different ways according to the desired tradeoff between image quality and time/energy savings. Experimental results presented a transrating time reduction of up to 57.5%, with a minimum energy consumption reduction of 49.5% and an average memory bandwidth reduction of 24% in comparison to the original transcoder. These results were achieved at the cost of a Bjontegaard delta (BD) rate increase of only 0.664% in the most conservative transrating mode, which allows a transrating time reduction of 48.5%. The proposed decision trees were implemented as an IP core and synthesized targeting 45-nm ASIC technology, achieving the capability of processing $7680\times 4320$ videos at 240 frames/s with a negligible power consumption of 0.849 mW.