The Last Byte: Learning Just Enough for Machine-Oriented Image Compression

Just recognizable distortion (JRD) has been introduced for image compression for machines, aiming to quantify the maximum coding distortion that can be tolerated by a specific perception model, thereby defining the upper bound of machine vision redundancy (MVR). However, existing JRD-based redundancy estimation methods face three key challenges: limited dataset annotation accuracy, low prediction efficiency, and insufficient perception accuracy, all of which hinder their practical deployment. To address these limitations, we propose a new MVR-Net, a frame-wise efficient JRD prediction method that generates the optimal encoding quantization map in a single inference pass. Furthermore, we refine the annotation standard for JRD datasets based on experimental insights, enhancing the precision of recognizable redundancy measurement. Compared to stateof-the-art methods, MVR-Net achieves a superior balance between bitrate reduction and perception accuracy in JRD-guided compression, while offering up to a 40,000× speed improvement, demonstrating its practicality and efficiency for real-world applications.

Authors

• Wuyuan Xie College of Computer Science and Software Engineering, Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University
• Zhenming Li College of Computer Science and Software Engineering, Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University
• Ye Liu
• Jian Jin Nanjing University of Science and Technology
• Yun Song School of Computer & Communication Engineering, Changsha University of Science & Technology, Changsha 410004
• Miaohui Wang College of Computer Science and Software Engineering, Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University

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