Currently, there is a high demand for neural network-based image compression codecs. These codecs employ non-linear transforms to create compact bit representations and facilitate faster coding speeds on devices compared to the hand-crafted transforms used in classical frameworks. The scientific and industrial communities are highly interested in these properties, leading to the standardization effort of JPEG-AI. The JPEG-AI verification model has been released and is currently under development for standardization. Utilizing neural networks, it can outperform the classic codec VVC intra by over 10% BD-rate operating at base operation point. Researchers attribute this success to the flexible bit distribution in the spatial domain, in contrast to VVC intra's anchor that is generated with a constant quality point. However, our study reveals that VVC intra displays a more adaptable bit distribution structure through the implementation of various block sizes. As a result of our observations, we have proposed a spatial bit allocation method to optimize the JPEG-AI verification model's bit distribution and enhance the visual quality. Furthermore, by applying the VVC bit distribution strategy, the objective performance of JPEG-AI verification mode can be further improved, resulting in a maximum gain of 0.45 dB in PSNR-Y.

翻译：当前，基于神经网络的图像压缩编解码器需求旺盛。此类编解码器采用非线性变换生成紧凑的比特表示，相较于经典框架中手工设计的变换，能在设备上实现更快的编码速度。科学界与工业界对其特性表现出高度兴趣，由此推动了JPEG-AI标准化工作的开展。JPEG-AI验证模型已发布并处于标准化开发阶段。利用神经网络技术，该模型在基础操作点上相较经典编解码器VVC intra实现了超过10%的BD-rate性能提升。研究者将此成功归因于空间域灵活比特分配的优势——与VVC intra通过恒定质量点生成的锚定方案形成鲜明对比。然而我们的研究发现，VVC intra通过采用不同块尺寸的编码策略，展现出更具自适应性的比特分配结构。基于上述观察，我们提出了一种空间比特分配方法，用于优化JPEG-AI验证模型的比特分布并提升视觉质量。此外，通过应用VVC比特分配策略，JPEG-AI验证模式的客观性能可进一步提升，在PSNR-Y指标上获得最高0.45dB的增益。