Abstract
The increased demand for high quality video evidently elevates the bandwidth requirements of the communication channels being used, which in return demands for more efficient video coding algorithms within the media distribution tool chain. As such, High Efficiency Video Coding (HEVC) video coding standard is a potential solution that demonstrates a significant coding efficiency improvement over its predecessors. HEVC constitutes an assortment of novel coding tools and features that contribute towards its superior coding performance, yet at the same time demand more computational, processing and energy resources; a crucial bottleneck, especially in the case of resource constrained Consumer Electronic (CE) devices. In this context, the first contribution in this thesis presents a novel content adaptive Coding Unit (CU) size prediction algorithm for HEVC-based low-delay video encoding. In this case, two independent content adaptive CU size selection models are introduced while adopting a moving window-based feature selection process to ensure that the framework remains robust and dynamically adapts to any varying video content. The experimental results demonstrate a consistent average encoding time reduction ranging from 55% - 58% and 57% - 61% with average Bjøntegaard Delta Bit Rate (BDBR) increases of 1.93% - 2.26% and 2.14% - 2.33% compared to the HEVC 16.0 reference software for the low delay P and low delay B configurations, respectively, across a wide range of content types and bit rates. The video decoding complexity and the associated energy consumption are tightly coupled with the complexity of the codec as well as the content being decoded. Hence, video content adaptation is extensively considered as an application layer solution to reduce the decoding complexity and thereby the associated energy consumption. In this context, the second contribution in this thesis introduces a decoding complexity-aware video encoding algorithm for HEVC using a novel decoding complexity-rate-distortion model. The proposed algorithm demonstrates on average a 29.43% and 13.22% decoding complexity reductions for the same quality with only a 6.47% BDBR increase when using the HM 16.0 and openHEVC decoders, respectively. Moreover, decoder energy consumption analysis reveals an overall energy reduction of up to 20% for the same video quality. Adaptive video streaming is considered as a potential solution in the state-of-the-art to cope with the uncertain fluctuations in the network bandwidth. Yet, the simultaneous consideration of both bit rate and decoding complexity for content adaptation with minimal quality impact is extremely challenging due to the dynamics of the video content. In response, the final contribution in this thesis introduces a content adaptive decoding complexity and rate controlled encoding framework for HEVC. The experimental results reveal that the proposed algorithm achieves a stable rate and decoding complexity controlling performance with an average error of only 0.4% and 1.78%, respectively. Moreover, the proposed algorithm is capable of generating HEVC bit streams that exhibit up to 20.03 %/dB decoding complexity reduction which result in up to 7.02 %/dB decoder energy reduction per 1dB Peak Signal-to-Noise Ratio (PSNR) quality loss.