Image prefiltering with just noticeable distortion (JND) improves coding efficiency in a visual lossless way by filtering the perceptually redundant information prior to compression. However, real JND cannot be well modeled with inaccurate masking equations in traditional approaches or image-level subject tests in deep learning approaches. Thus, this paper proposes a fine-grained JND prefiltering dataset guided by image quality assessment for accurate block-level JND modeling. The dataset is constructed from decoded images to include coding effects and is also perceptually enhanced with block overlap and edge preservation. Furthermore, based on this dataset, we propose a lightweight JND prefiltering network, IQNet, which can be applied directly to different quantization cases with the same model and only needs 3K parameters. The experimental results show that the proposed approach to Versatile Video Coding could yield maximum/average bitrate savings of 41\%/15\% and 53\%/19\% for all-intra and low-delay P configurations, respectively, with negligible subjective quality loss. Our method demonstrates higher perceptual quality and a model size that is an order of magnitude smaller than previous deep learning methods.

翻译：图像预滤波通过恰可察觉失真（JND）在压缩前滤除感知冗余信息，以视觉无损方式提升编码效率。然而，传统方法中不精确的掩膜方程或深度学习方法中的图像级主观测试难以有效建模真实JND。为此，本文提出一种由图像质量评估引导的细粒度JND预滤波数据集，用于精确的块级JND建模。该数据集由解码图像构建以包含编码效应，并通过块重叠与边缘保留进行感知增强。此外，基于该数据集，我们提出轻量级JND预滤波网络IQNet，该网络仅需3K参数即可用同一模型直接适配不同量化场景。实验结果表明，该方法在全帧内和低延迟P配置下，可为多功能视频编码分别节省最大/平均比特率41%/15%和53%/19%，且主观质量损失可忽略。与先前深度学习方法相比，本方法具有更高感知质量，且模型规模小一个数量级。