Perceptual VVC quantization refinement with ensemble learning
| Autor: | Hongan Wei, Yuxuan Wu, Zheng Wang, Liqun Lin, Tiesong Zhao, Weiling Chen |
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| Rok vydání: | 2021 |
| Předmět: |
Computer science
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Ensemble learning Human-Computer Interaction Constraint (information theory) Reduction (complexity) Hardware and Architecture Human visual system model Redundancy (engineering) Quality of experience Electrical and Electronic Engineering Quantization (image processing) Algorithm Coding (social sciences) |
| Zdroj: | Displays. 70:102103 |
| ISSN: | 0141-9382 |
| DOI: | 10.1016/j.displa.2021.102103 |
| Popis: | Compressing videos while maintaining an acceptable level of Quality of Experience (QoE) is indispensable. To this aim, a feasible method is to further increase the Quantization Parameter (QP) of video stream to eliminate visual redundancy, simultaneously utilizing perceptual characteristics of Human Visual System (HVS) to impose a threshold constraint on the maximum QP. In this paper, we employ Just Noticeable Distortion (JND) to characterize the aforementioned threshold constraint, thereby avoiding perceptual loss during QP refinement process. We propose an effective JND-based algorithm for QP optimization, in which a video saliency detection is introduced to extract regions of interest, a refinement model based on a lightweight network is designed to predict QP value and an ensemble learning method to improve generalization performance. Theoretical analysis and experimental results demonstrate that the proposed algorithm has been successfully applied to Versatile Video Coding (VVC) to achieve significant bitrate reduction without sacrificing perceived quality. |
| Databáze: | OpenAIRE |
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