  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.
