Improving Deep Learning Classification of JPEG2000 Images Over Bandlimited Networks

JPEG2000 (j2k) is a highly popular format for image and video compression. It plays a major role in the rapidly growing applications of cloud based image classification. Considering limited network bandwidth, we propose an end-to-end deep learning framework to achieve faster and more accurate classification by directly training a deep CNN image classifier using the CDF 9/7 Discrete Wavelet Transformed (DWT) coefficients from j2k-compressed images without image reconstruction. We demonstrate additional computation savings by utilizing shallower CNN to achieve classification of good accuracy. Furthermore, we present DWT-centric augmentation transformations to achieve more accurate classification without added cost. Achieving faster and more accurate classification for j2k encoded images, the proposed solution is well suited for joint compression and cloud-based image and video classification over limited channel bandwidth.