SVM based approach for complexity control of HEVC intra coding

The High Efficiency Video Coding (HEVC) is adopted by various video applications in recent years. Because of its high computational demand, controlling the complexity of HEVC is of paramount importance to appeal to the varying requirements in many applications, including power-constrained video coding, video streaming, and cloud gaming. Most of the existing complexity control methods are only capable of considering a subset of the decision space, which leads to low coding efficiency. While the efficiency of machine learning methods such as Support Vector Machines (SVM) can be employed for higher precision decision making, the current SVM-based techniques for HEVC provide a fixed decision boundary which results in different coding complexities for different video content. Although this might be suitable for complexity reduction, it is not acceptable for complexity control. This paper proposes an adjustable classification approach for Coding Unit (CU) partitioning, which addresses the mentioned problems of complexity control. Firstly, a novel set of features for fast CU partitioning is designed using image processing techniques. Then, a flexible classification method based on SVM is proposed to model the CU partitioning problem. This approach allows adjusting the performance-complexity trade-off, even after the training phase. Using this model, and a novel adaptive thresholding technique, an algorithm is presented to deliver video encoding within the target coding complexity, while maximizing the coding efficiency. Experimental results justify the superiority of this method over the state-of-the-art methods, with target complexities ranging from 20% to 100%.acceptedVersionPeer reviewe

Energy Consumption Analysis and Modelling of a H.264/AVC Intra-only Based Encoder Dedicated to WVSNs

International audienceThis paper proposes a model to predict the energy consumption of a H.264/AVC intra-only based encoder designed for the wireless video sensor networks (WVSNs). Such model is of great interest in an energy-constrained context like the WVSN, where the video streams are processed prior to transmission. In fact, accounting for both processing's and transmission's consumed energies allows the optimization of the global energy consumption. The proposed model predicts the processing's energy consumption based on the considered Quantization Parameter (QP) and the Frame Rate (FR) values. The generic form of this model enables it to predict the energy consumption of any H.264/AVC intra-only based encoder. Simulation results demonstrate the accuracy of the proposed model, that is validated under different resolutions, QP and FR, with an average prediction error of 4%