In recent years, there has been rapid development in learned image compression techniques that prioritize ratedistortion-perceptual compression, preserving fine details even at lower bit-rates. However, current learning-based image compression methods often sacrifice human-friendly compression and require long decoding times. In this paper, we propose enhancements to the backbone network and loss function of existing image compression model, focusing on improving human perception and efficiency. Our proposed approach achieves competitive subjective results compared to state-of-the-art end-to-end learned image compression methods and classic methods, while requiring less decoding time and offering human-friendly compression. Through empirical evaluation, we demonstrate the effectiveness of our proposed method in achieving outstanding performance, with more than 25% bit-rate saving at the same subjective quality.

翻译：近年来，优先考虑率失真感知压缩的学习图像压缩技术发展迅速，即使以较低比特率也能保留精细细节。然而，当前基于学习的图像压缩方法常牺牲人性化压缩且要求较长的解码时间。本文对现有图像压缩模型的骨干网络与损失函数进行了改进，聚焦于提升人类感知与效率。与最先进的端到端学习图像压缩方法及经典方法相比，本方法在实现竞争性主观效果的同时，所需解码时间更短且具备人性化压缩特性。通过经验评估，我们验证了本方法在实现卓越性能方面的有效性，在同等主观质量下可实现超过25%的比特率节省。