Abstract
Providing adequate Quality of Experience (QoE) to end-users is crucial for streaming service providers. In this paper, in order to realize automatic quality assessment, a No-Reference (NR) bitstream Human-Vision-System-(HVS)-based video quality assessment (VQA) model is proposed. Inspired by discoveries from the neuroscience community, which suggest there is a considerable overlap between active areas of the brain when engaging in video quality assessment and saliency detection tasks, saliency maps are used in the proposed method to improve the quality assessment accuracy. To this end, saliency maps are first generated from features extracted from the HEVC bitstream. Then, saliency map statistics are employed to create a model of visual memory. Finally, a support vector regression pipeline learns an estimate of the video quality from the visual memory, saliency, and frame features. Evaluations on SJTU dataset indicate that the proposed bitstream based no-reference video quality assessment algorithm achieves a competitive performance.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Visual Communication and Image Representation
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
