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。

高保真神经图像压缩的语义集成损失和潜在精炼

Semantic Ensemble Loss and Latent Refinement for High-Fidelity Neural  Image Compression

For any video codecs, the coding efficiency highly relies on whether the
current signal to be encoded can find the relevant contexts from the previous
reconstructed signals. Traditional codec has verified more contexts bring
substantial coding gain, but in a time-consuming manner. However, for the
emerging neural video codec (NVC), its contexts are still limited, leading to
low compression ratio. To boost NVC, this paper proposes increasing the context
diversity in both temporal and spatial dimensions. First, we guide the model to
learn hierarchical quality patterns across frames, which enriches long-term and
yet high-quality temporal contexts. Furthermore, to tap the potential of
optical flow-based coding framework, we introduce a group-based offset
diversity where the cross-group interaction is proposed for better context
mining. In addition, this paper also adopts a quadtree-based partition to
increase spatial context diversity when encoding the latent representation in
parallel. Experiments show that our codec obtains 23.5% bitrate saving over
previous SOTA NVC. Better yet, our codec has surpassed the under-developing
next generation traditional codec/ECM in both RGB and YUV420 colorspaces, in
terms of PSNR. The codes are at this https URL

提出增强神经视频编解码器的上下文多样性，分别从时间和空间维度引入了层次质量模式、基于光流的编码框架以及四叉树分区以增加上下文多样性，实验结果表明，相比现有技术，该编解码器的比特率节省达到 23.5％，并在 PSNR 方面超越了传统编解码器 / ECM 的下一代。

多元背景神经视频压缩

Neural Video Compression with Diverse Contexts

Most of the existing neural video compression methods adopt the predictive
coding framework, which first generates the predicted frame and then encodes
its residue with the current frame. However, as for compression ratio,
predictive coding is only a sub-optimal solution as it uses simple subtraction
operation to remove the redundancy across frames. In this paper, we propose a
deep contextual video compression framework to enable a paradigm shift from
predictive coding to conditional coding. In particular, we try to answer the
following questions: how to define, use, and learn condition under a deep video
compression framework. To tap the potential of conditional coding, we propose
using feature domain context as condition. This enables us to leverage the high
dimension context to carry rich information to both the encoder and the
decoder, which helps reconstruct the high-frequency contents for higher video
quality. Our framework is also extensible, in which the condition can be
flexibly designed. Experiments show that our method can significantly
outperform the previous state-of-the-art (SOTA) deep video compression methods.
When compared with x265 using veryslow preset, we can achieve 26.0% bitrate
saving for 1080P standard test videos.

本文提出了一种基于深度上下文视频压缩框架的条件编码方案，通过特征域上下文作为条件来利用高维的上下文，从而在视频质量更高的情况下实现大幅度的比特率节省。