Theperception-distortion- tradeoffreveals the limitation of current low-level deep learning paradigms,i.e., minimizing reconstruction distortion does not guarantee improved perceptual quality. Acknowledging the lack of a reliableperception-oriented optimization function, we are motivated to explore a flexible approach for enhancing perceptual quality by steering thetradeoffto prioritizeperception. To this end, we reconsider theperception-distortionfunction by incorporating the Just-Noticeable-Distortion (JND) mechanism. We mathematically demonstrate that in the common image restoration process, altering the optimization target from natural images to distorted images—where the distortion intensity is constrained by the JND threshold and the distortion type aligns with that arising from the restorer itself—effectively obtained improvedperceptionindices without any changes to the restorer or optimization function. Accordingly, to facilitate various low-level learning models, we are motivated to construct the first large-scale CNN-oriented JND image dataset. Our dataset comprises 500 natural images and 4,500 degraded versions generated by a series of autoencoders, as well as the actual JND judgment results collected through rigorous subjective testing from twenty volunteers. Finally, a learning-based JND inference model is established on the proposed dataset and employed in the proposed JND-based adaptation scheme, where the inferred JND images serve as pseudo-ground truth for the training or fine-tuning processes of low-level vision models. Extensive experiments on image super-resolution and end-to-end image compression across multiple models have shown encouraging improvements in perceptual quality, demonstrating the effectiveness of the proposed scheme. Our dataset is available at:https://github.com/ohq17/CNN-Oriented-JND-Dataset.

Among various technical approaches in machine vision coding, Image Coding for Machine (ICM) stands out for its capability to simultaneously fulfill both human perception and machine vision needs. However, it is often criticized for its lack of efficiency regarding rate-analytics performance. In this paper, we propose an Appearance Redundancy Reduction (ARR) module, designed to function as a plug-in for existing ICM frameworks, aiming to further enhance the coding efficiency regarding rate analytics without any changes to the ICM itself. To be specific, our work pays additional attention to the intrinsic correlation between the low-level image structure and high-level vision analytics, and subsequently proposes a novel colour quantization mechanism to squeeze out the analytics-free redundant appearance information. Moreover, a differentiable soften quantization operation is derived to enable end-to-end training within the ICM framework. Extensive experimental results have shown that integrating the proposed ARR module yields substantial improvements regarding rate-analytic performance, even surpassing the performance of the feature coding paradigm, while maintaining the generalizability across different tasks and acceptable perceptual representation.