Learning-based image compression methods have emerged as state-of-the-art, showcasing higher performance compared to conventional compression solutions. These data-driven approaches aim to learn the parameters of a neural network model through iterative training on large amounts of data. The optimization process typically involves minimizing the distortion between the decoded and the original ground truth images. This paper focuses on perceptual optimization of learning-based image compression solutions and proposes: i) novel loss function to be used during training and ii) novel subjective test methodology that aims to evaluate the decoded image fidelity. According to experimental results from the subjective test taken with the new methodology, the optimization procedure can enhance image quality for low-rates while offering no advantage for high-rates.

翻译：基于学习的图像压缩方法已成为最先进的技术，相较于传统压缩方案展现出更高的性能。这些数据驱动的方法旨在通过大量数据的迭代训练学习神经网络模型的参数。优化过程通常涉及最小化解码图像与原始真实图像之间的失真。本文聚焦于基于学习的图像压缩方案的感知优化，并提出：i）训练过程中使用的新型损失函数，以及ii）旨在评估解码图像保真度的新型主观测试方法。根据采用新方法进行的主观测试实验结果表明，该优化流程能够提升低码率下的图像质量，但在高码率下未显示优势。