Recent advancements in neural compression have surpassed traditional codecs
in PSNR and MS-SSIM measurements. However, at low bit-rates, these methods can
introduce visually displeasing artifacts, such as blurring, color shifting, and
texture loss, thereby compromising perceptual quality of images. To address
these issues, this study presents an enhanced neural compression method
designed for optimal visual fidelity. We have trained our model with a
sophisticated semantic ensemble loss, integrating Charbonnier loss, perceptual
loss, style loss, and a non-binary adversarial loss, to enhance the perceptual
quality of image reconstructions. Additionally, we have implemented a latent
refinement process to generate content-aware latent codes. These codes adhere
to bit-rate constraints, balance the trade-off between distortion and fidelity,
and prioritize bit allocation to regions of greater importance. Our empirical
findings demonstrate that this approach significantly improves the statistical
fidelity of neural image compression. On CLIC2024 validation set, our approach
achieves a 62% bitrate saving compared to MS-ILLM under FID metric.

该研究提出了一种改进的神经压缩方法，通过使用复杂的语义集成损失、潜在精细化过程和优化可视保真度的目标，显著提高了神经图像压缩的统计保真度，并在 CLIC2024 验证集上相较于 MS-ILLM 在 FID 指标下实现了 62% 的比特率节省。