Deep Selector-JPEG: Adaptive JPEG Image Compression for Computer Vision in Image classification with Human Vision Criteria

TL;DR

Deep Selector-JPEG adaptively compresses images for classification tasks by balancing compression ratio and accuracy while respecting human vision quality, outperforming standard JPEG in accuracy gains.

Contribution

Introduces an adaptive JPEG compression method tailored for DNN-based image classification that considers human vision criteria, improving rate-accuracy trade-offs.

Findings

Achieves 0.2% to 1% higher accuracy than JPEG at the same compression ratio.

Maintains high MS-SSIM values, satisfying human vision quality constraints.

Provides near-original classification accuracy at higher compression ratios.

Abstract

With limited storage/bandwidth resources, input images to Computer Vision (CV) applications that use Deep Neural Networks (DNNs) are often encoded with JPEG that is tailored to Human Vision (HV). This paper presents Deep Selector-JPEG, an adaptive JPEG compression method that targets image classification while satisfying HV criteria. For each image, Deep Selector-JPEG selects adaptively a Quality Factor (QF) to compress the image so that a good trade-off between the Compression Ratio (CR) and DNN classifier Accuracy (Rate-Accuracy performance) can be achieved over a set of images for a variety of DNN classifiers while the MS-SSIM of such compressed image is greater than a threshold value predetermined by HV with a high probability. Deep Selector-JPEG is designed via light-weighted or heavy-weighted selector architectures. Experimental results show that in comparison with JPEG at the same CR, Deep Selector-JPEG achieves better Rate-Accuracy performance over the ImageNet validation set for all tested DNN classifiers with gains in classification accuracy between 0.2% and 1% at the same CRs while satisfying HV constraints. Deep Selector-JPEG can also roughly provide the original classification accuracy at higher CRs.