Just Recognizable Difference (JRD) represents the minimum visual difference that is detectable by machine vision, which can be exploited to promote machine vision oriented visual signal processing. In this paper, we propose a Deep Transformer based JRD (DT-JRD) prediction model for Video Coding for Machines (VCM), where the accurately predicted JRD can be used reduce the coding bit rate while maintaining the accuracy of machine tasks. Firstly, we model the JRD prediction as a multi-class classification and propose a DT-JRD prediction model that integrates an improved embedding, a content and distortion feature extraction, a multi-class classification and a novel learning strategy. Secondly, inspired by the perception property that machine vision exhibits a similar response to distortions near JRD, we propose an asymptotic JRD loss by using Gaussian Distribution-based Soft Labels (GDSL), which significantly extends the number of training labels and relaxes classification boundaries. Finally, we propose a DT-JRD based VCM to reduce the coding bits while maintaining the accuracy of object detection. Extensive experimental results demonstrate that the mean absolute error of the predicted JRD by the DT-JRD is 5.574, outperforming the state-of-the-art JRD prediction model by 13.1%. Coding experiments shows that comparing with the VVC, the DT-JRD based VCM achieves an average of 29.58% bit rate reduction while maintaining the object detection accuracy.

翻译：可识别差异（JRD）表示机器视觉可检测到的最小视觉差异，可用于促进面向机器视觉的视觉信号处理。本文针对机器视频编码（VCM）提出一种基于深度Transformer的JRD（DT-JRD）预测模型，通过准确预测JRD可在保持机器任务精度的同时降低编码比特率。首先，我们将JRD预测建模为多类别分类问题，提出一种集成改进嵌入模块、内容与失真特征提取模块、多类别分类模块及新型学习策略的DT-JRD预测模型。其次，受机器视觉对JRD附近失真具有相似响应特性的启发，我们提出采用基于高斯分布的软标签（GDSL）构建渐进式JRD损失函数，该函数显著扩展了训练标签数量并松弛了分类边界。最后，我们提出基于DT-JRD的VCM方案，在保持目标检测精度的同时降低编码比特。大量实验结果表明，DT-JRD预测JRD的平均绝对误差为5.574，较现有最优JRD预测模型提升13.1%。编码实验表明，相较于VVC标准，基于DT-JRD的VCM方案在保持目标检测精度的同时平均可实现29.58%的比特率降低。