No-Reference Deep Compressed-Based Video Quality Assessment

Abstract

A novel No-Reference Video Quality Assessment (NR-VQA), based on Convolutional Neural Network (CNN) for High Efficiency Video Codec (HEVC) is presented. Deep Compressed-domain Video Quality (DCVQ) measures the video quality, with compressed domain features such as motion vector, bit allocation, partitioning and quantization parameter. For the training of the network, P-MOS is used due to the limitation of existing datasets. The evaluation of the proposed method shows that it has “96%” correlation to subjective quality assessment (MOS). The method can work simultaneously with the decoding process and measures the quality in different resolutions.