Abstract
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.
Abstract (translated)
基于神经网络（NN）的图像压缩研究已经表明，与经典压缩框架相比具有卓越的性能。与经典框架中的手工程变换不同，NN模型通过学习非线性变换提供更加紧凑的比特表示，并在并行设备上实现更快的学生速度。这些特性引起了科学和工业界的关注，从而促进了JPEG-AI标准化活动的开展。JPEG-AI标准化过程的验证模型已经在开发中，并已经超越了先进的VVC内部编码器。为了生成目标比特每像素的图像，并评估JPEG-AI验证模型和VVC内部编码器的BD-rate性能，采用位率匹配。然而，在位率匹配过程中，JPEG-AI验证模型的状态出现了显著的减速，导致由于不合适的模型导致的性能 suboptimal。所提出的方法提供了一种逐步算法优化位率匹配，从而实现四倍加速和基操作点处超过1%的BD-rate改善。在高端操作点，加速会增加至六倍。
URL
https://arxiv.org/abs/2402.17487
