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.

在保持可接受的体验质量 (QoE) 水平的同时压缩视频是必不可少的。为此，一种可行的方法是进一步增加视频流的量化参数（QP）以消除视觉冗余，同时利用人类视觉系统（HVS）的感知特性对最大QP施加阈值约束。在本文中，我们使用 Just Noticeable Distortion (JND) 来表征上述阈值约束，从而避免 QP 细化过程中的感知损失。我们提出了一种有效的基于 JND 的 QP 优化算法，其中引入了视频显着性检测来提取感兴趣区域，设计了基于轻量级网络的细化模型来预测 QP 值，并设计了一种集成学习方法来提高泛化性能。