Fast CU Size Decision Method Based on Just Noticeable Distortion and Deep Learning

With the development of broadband networks and high-definition displays, people have higher expectations for the quality of video images, which also brings new requirements and challenges to video coding technology. Compared with H.265/High Efficiency Video Coding (HEVC), the latest video coding standard, Versatile Video Coding (VVC), can save 50%-bit rate while maintaining the same subjective quality, but it leads to extremely high encoding complexity. To decrease the complexity, a fast coding unit (CU) size decision method based on Just Noticeable Distortion (JND) and deep learning is proposed in this paper. Specifically, the hybrid JND threshold model is first designed to distinguish smooth, normal, or complex region. Then, if CU belongs to complex area, the Ultra-Spherical SVM (US-SVM) classifiers are trained for forecasting the best splitting mode. Experimental results illustrate that the proposed method can save about 52.35% coding runtime, which can realize a trade-off between the reduction of computational burden and coding efficiency compared with the latest methods.

A Fast CU Size Decision Optimal Algorithm based on Coding Bits for HEVC

HEVC (High Efficiency Video Codng) employs quadtree CTU (Coding Tree Unit) structure to improve its coding efficiency, but at the same time, it also requires a very high computational complexity due to its exhaustive search process for an optimal partition mode for the current CU(Coding Unit). Aiming to solve the problem, a fast CU size decision optimal algorithm based on coding bits is presented for HEVC in this paper. The contribution of this paper lies that we successfully use the coding bits technology to quickly determine the optimal partition mode for the current CU. Specially, in our scheme, firstly we carefully observe and statistically analyze the relationship among the texture complexity and partition size and coding bits in the CUs of video image; Secondly we find the correlation between coding bits and partition size based on the relationship above; Thirdly, we build the corresponding threshold of coding bits for partition size under different CU size and QP value based on the correlation above to reduce many unnecessary prediction and partition operations for the current CU. As a result, our proposed algorithm can effectively saving lots of computational complexity for HEVC. The simulation results show that our proposed fast CU size decision algorithm based on coding bits in this paper can save about 34.67% coding time, and only at a cost of 0.61% BDBR increase and 0.043db BDPSNR decline compared with the standard reference of HM16.1, thus improving the coding performance of HEVC.