High efficiency video coding (HEVC) doubles the compression ratio as compared to H.264/AVC, for the same quality. To achieve this improved coding performance, HEVC presents a new content-adaptive approach to split a frame into coding units (CUs), along with an increased number of prediction modes, which results in significant computational complexity. To lower this complexity with intra coding, in this paper, we develop a new method based on global and directional gradients to terminate the CU splitting procedure early and prevent processing of unnecessary depths. The global and directional gradients determine if the unit is predicted with high accuracy at the current level, and where that's the case, the CU is deemed to be non-split. Experimental results show that the proposed method reduces the encoding time by 52% on average, with a small quality loss of 0.07 dB (BD-PSNR) for all-intra scenarios, as compared to the HEVC reference implementation, HM 15.0.

/pdf/coding-unit-splitting-early-termination-for-fast-hevc-intra-h0avfn5q02.pdf

Coding unit splitting early termination for fast HEVC intra coding based on global and directional gradients

With the increasing use of digital video, the importance of establishing a high-performance no reference video quality assessment (NR VQA) model is increasing. How to effectively assess the properties of the human visual system (HVS) in a data-driven manner is one of the difficulties in NR VQA. In this paper, we propose a spatio-temporal network model based on saliency. The model has two branches: the spatio-temporal branch and the saliency branch. We propose a basic spatio-temporal network model in the spatio-temporal branch and use it to predict the score after the spatial distortion effected by temporal information. Then we extract the salient features of the current video frame and merge it with the spatial distortion to predict a result that is more in line with human perception. Finally, the two scores are automatically weighted to obtain the score of the current video. The performance of the proposed method has been verified on two databases, LIVE and CSIQ. The training results show that the method proposed in this paper can basically conform to the subjective perception of human eyes, and the performance of the network can also be better than most current methods without reference video.

Spatial-Temporal Network for No Reference Video Quality Assessment Based on Saliency

Video quality assessment combining with human visual gaze characteristics

High efficiency video coding (HEVC) utilizes several technology innovations to improve encoding efficiency. However, it also results in heavy computational complexity. A coding unit (CU) splitting early termination algorithm is proposed to save the encoding time of HEVC intra coding. Firstly, CU size distribution is statistically analyzed, and the CU splitting early termination algorithm for Intra coding is proposed. Then, the threshold in the proposed algorithm, which varies with the factors, CU sizes, QPs and sequences, is designed based on modified MRBD rule to achieve a better rate distortion performance. Finally, the proposed algorithm is estimated. Experimental results show that the proposed algorithm saves 31.71% encoding time on average, while it hardly deteriorates the coding efficiency.

CU Splitting Early Termination Algorithm in HEVC Intra Coding

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Video quality assessment combining with human visual gaze characteristics

CU Splitting Early Termination Algorithm in HEVC Intra Coding