  The popularity of Convolutional Neural Network (CNN) in the field of Image
Processing and Computer Vision has motivated researchers and industrialist
experts across the globe to solve different challenges with high accuracy. The
simplest way to train a CNN classifier is to directly feed the original RGB
pixels images into the network. However, if we intend to classify images
directly with its compressed data, the same approach may not work better, like
in case of JPEG compressed images. This research paper investigates the issues
of modifying the input representation of the JPEG compressed data, and then
feeding into the CNN. The architecture is termed as DCT-CompCNN. This novel
approach has shown that CNNs can also be trained with JPEG compressed DCT
coefficients, and subsequently can produce a better performance in comparison
with the conventional CNN approach. The efficiency of the modified input
representation is tested with the existing ResNet-50 architecture and the
proposed DCT-CompCNN architecture on a public image classification datasets
like Dog Vs Cat and CIFAR-10 datasets, reporting a better performance
