Semantic Ensemble Loss and Latent Refinement for High-Fidelity Neural
  Image Compression
Daxin Li Yuanchao Bai Kai Wang Junjun Jiang Xianming Liu
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.