A perceptual image compression framework is proposed in this work, including an adaptive picture-level just noticeable difference (PJND) prediction model and a perceptual coding scheme. Specifically speaking, a convolutional neural network (CNN) model is designed with the existing subjective image database to predict the PJND label for a given image. Then, the support vector regression model is utilized to determine the number of PJND levels. After that, a just noticeable difference generation algorithm is developed to compute the corresponding quality factor for each PJND level. Moreover, an effective perceptual coding scheme is devised for perceptual image compression. Finally, the accuracy of the proposed PJND prediction model and the performance of the proposed perceptual coding scheme are evaluated. The experimental results show that the proposed CNN based PJND prediction model achieves good prediction accuracy and the proposed perceptual coding scheme produces state-of-the-art rate distortion performances.

中文翻译：

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感知图像压缩的显着差异水平预测

在这项工作中提出了一种感知图像压缩框架，包括自适应图片级刚注意到差异（PJND）预测模型和感知编码方案。具体来说，就是利用现有的主观图像数据库设计卷积神经网络 (CNN) 模型来预测给定图像的 PJND 标签。然后，利用支持向量回归模型来确定 PJND 级别的数量。之后，开发了一个恰到好处的差异生成算法来计算每个 PJND 级别的相应质量因子。此外，为感知图像压缩设计了一种有效的感知编码方案。最后，评估了所提出的 PJND 预测模型的准确性和所提出的感知编码方案的性能。