江苏大学学报（自然科学版）2026，Vol.47Issue(2)：189-197,9.DOI:10.3969/j.issn.1671-7775.2026.02.009
机器视觉最小可察觉误差的建模及其优化
Modeling and optimization of just noticeable difference for machine vision
摘要
Abstract
To improve the accuracy and reduce the complexity of the just noticeable difference(JND)model for machine vision,the framework design of existing JND models and the design of task accuracy loss,magnitude loss and spatial distribution loss in the model training constraints were investigated,and the improved machine vision JND model of Smooth-DMV-JND was proposed.The major components of the model were composed of three convolutional neural networks parts with saliency analysis module,edge extraction module and fusion analysis module,and the proposed model was trained with modified objective function by the designed two-stage training pipeline.The performance and complexity of the model were analyzed through experiments,and the application of Smooth-DMV-JND in image compression was presented.The results show that the proposed Smooth-DMV-JND model can provide more accurate estimation of JND for machine vision with shorter analysis time.Smoothed by the Smooth-DMV-JND model with maintaining about 88％classification accuracy,the processed images achieve bit-rate saving values of 17.68％for JPEG compression and 10.69％for BPG compression than that compressing the originals directly.The JND for machine vision can be modeled more effectively by Smooth-DMV-JND,which can guide the removal of redundancy in images and is beneficial to machine vision-oriented image compression.关键词
机器视觉/最小可察觉误差/图像压缩/图像感知/卷积神经网络/图像平滑Key words
machine vision/just noticeable difference/image compression/image understanding/convolutional neural network/image smooth分类
信息技术与安全科学引用本文复制引用
蒋伟,肖睿,魏春娟..机器视觉最小可察觉误差的建模及其优化[J].江苏大学学报（自然科学版）,2026,47(2):189-197,9.基金项目
国家自然科学基金资助项目(61401269) （61401269）
