A Fast Multi-Type Tree Decision Algorithm for VVC Based on Pixel Difference of Sub-Blocks

Li, Zhi; Dong, Mengjun; Zhang, Mengmeng

doi:10.1587/transfun.2020eal2008

Cited by 2 publications

(1 citation statement)

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“…A deep Convolutional Neural Networks (CNN) model-based fast QT partition method is developed in [9] to forecast CU splitting mode, which considerably enhances performance. A fast MTT decision method is designed in [10], which can decrease computational complexity and maintain compression performance. Specifically, the splitting decision mode can be early decided by comparing the pixel difference of subblocks (SBPD) in horizontal and vertical subblocks so as to skip some redundant splitting modes.…”

Section: Related Workmentioning

confidence: 99%

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

Zhao

Wang

Zhang

2021

Scientific Programming

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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.

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Section: Related Workmentioning

confidence: 99%