The research on neural network (NN) based image compression has shown superior performance compared to classical compression frameworks. Unlike the hand-engineered transforms in the classical frameworks, NN-based models learn the non-linear transforms providing more compact bit representations, and achieve faster coding speed on parallel devices over their classical counterparts. Those properties evoked the attention of both scientific and industrial communities, resulting in the standardization activity JPEG-AI. The verification model for the standardization process of JPEG-AI is already in development and has surpassed the advanced VVC intra codec. To generate reconstructed images with the desired bits per pixel and assess the BD-rate performance of both the JPEG-AI verification model and VVC intra, bit rate matching is employed. However, the current state of the JPEG-AI verification model experiences significant slowdowns during bit rate matching, resulting in suboptimal performance due to an unsuitable model. The proposed methodology offers a gradual algorithmic optimization for matching bit rates, resulting in a fourfold acceleration and over 1% improvement in BD-rate at the base operation point. At the high operation point, the acceleration increases up to sixfold.

翻译：基于神经网络（NN）的图像压缩研究已展现出优于经典压缩框架的性能。与经典框架中手工设计的变换不同，NN模型通过学习非线性变换实现更紧凑的比特表示，并在并行设备上获得比经典方法更快的编码速度。这些特性引起了科学界和工业界的广泛关注，催生了JPEG-AI标准化活动。当前JPEG-AI标准化流程的验证模型已进入开发阶段，其性能超越了先进的VVC帧内编码器。为生成具有目标每像素比特数的重建图像并评估JPEG-AI验证模型与VVC帧内编码器的BD-rate性能，需采用比特率匹配技术。然而，当前JPEG-AI验证模型在比特率匹配过程中存在显著减速问题，且因模型不适用导致性能欠佳。本文提出的方法实现了比特率匹配的渐进式算法优化，在基础操作点实现四倍加速并提升BD-rate超1%，在高端操作点加速效果可达六倍。