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.

当前，对基于神经网络的图像压缩编解码器有很高的需求。这些编解码器采用非线性变换创建紧凑的位表示，并通过在设备上实现快速编码速度，与经典框架中使用的手工设计变换相比，科研和工业界对这些特性非常感兴趣，推动了 JPEG-AI 的标准化工作。通过利用神经网络，该模型在基本运行点上超过传统编解码器的 10% BD 率，并通过在空间域中灵活分配位分布，与 VVC intra 的锚点生成恒定质量点的方式形成鲜明对比。然而，我们的研究发现，通过实现各种块尺寸，VVC intra 显示出更具适应性的位分布结构。由于我们的观察结果，我们提出了一种空间位分配方法来优化 JPEG-AI 的位分布并提高视觉质量。此外，通过应用 VVC 的位分布策略，可以进一步提高 JPEG-AI 的客观性能，使 PSNR-Y 获得最大增益 0.45 dB。