Image coding for machines (ICM) aims at reducing the bitrate required to
represent an image while minimizing the drop in machine vision analysis
accuracy. In many use cases, such as surveillance, it is also important that
the visual quality is not drastically deteriorated by the compression process.
Recent works on using neural network (NN) based ICM codecs have shown
significant coding gains against traditional methods; however, the decompressed
images, especially at low bitrates, often contain checkerboard artifacts. We
propose an effective decoder finetuning scheme based on adversarial training to
significantly enhance the visual quality of ICM codecs, while preserving the
machine analysis accuracy, without adding extra bitcost or parameters at the
inference phase. The results show complete removal of the checkerboard
artifacts at the negligible cost of -1.6% relative change in task performance
score. In the cases where some amount of artifacts is tolerable, such as when
machine consumption is the primary target, this technique can enhance both
pixel-fidelity and feature-fidelity scores without losing task performance.

图像编码技术旨在降低图像表示所需的比特率，同时最大程度地减少机器视觉分析准确性的下降。本研究提出了一种基于对抗训练的有效解码器微调方案，以显著提高图像编码技术的视觉质量，同时保持机器分析准确性，无需在推理阶段增加额外的比特率或参数。实验结果表明，在忽略任务性能分数相对变化 - 1.6% 的情况下，完全消除了棋盘格伪影。对于一些可容忍某些伪影的情况，例如机器消费是主要目标的情况，该技术可以提高像素保真度和特征保真度分数，同时不影响任务性能。