H.265 intra-picture prediction acceleration using low complexity model

Abstract

H.265 also called High Efficiency Video Coding is the new futuristic international standard proposed by Joint collaboration Team on Video Coding and released in 2013 in the view of constantly increasing demand of video applications. This new standard reduces the bitrate to half as compared to its predecessor H.264 at the expense of huge amount of computational burden on the encoder. In the proposed work we focus on intraprediction phase of video encoding where 33 new angular modes are introduced in addition to DC and Planar mode in order to achieve high quality videos at higher resolutions. We have proposed the use of applied machine learning to HEVC intra prediction to accelerate angular mode decision process. The features used are also low complexity features with minimal computation so as to avoid any additional burden on the encoder. The Decision tree model built is simple yet efficient which is the requirement of the complexity reduction scenario. The proposed method achieves substantial average encoding time saving of 86.59%, with QP values 4,22,27,32 respectively with minimal loss of 0.033 of PSNR and 0.0023 loss in SSIM which makes it suitable for acceptance of High Efficiency Video coding in real time applications.