Discrete Atomic Compression of JPEG Images: A Comparative Study with a Progressive DCT-Based Coder and Evaluation Across a Diverse Set of Established and State-of-the-Art CNN Classifiers

Large-scale image databases are continuously being accumulated across diverse information systems, and JPEG is the de facto standard for storing full-color images. We consider the problem of reducing the storage expenses of large volumes of photorealistic JPEG images with controlled distortion levels and without significant degradation in subsequent classification accuracy of convolutional neural networks. To address this problem, we apply the Discrete Atomic Compression (DAC) algorithm, which is based on atomic functions. Prior work has shown that this method combines compression with image content protection in a way that is relevant to modern secure image processing. For comparison, we also include a Progressive DCT-based Coder (PDCTC). Experiments are conducted on the ImageNet validation set (50,000 images across 1000 classes). Compression performance is assessed using MAD, RMS, PSNR, and compression ratio (CR). The compressed images are then classified by 28 pre-trained CNN classifiers spanning eight architectural families from established (VGG, ResNet) to state-of-the-art (EfficientNetV2) models. Classification is evaluated using Top-1 and Top-5 accuracy, among other metrics. DAC achieves a mean compression ratio of 1.93 relative to JPEG and PDCTC achieves 1.58. Both methods provide high reconstruction quality (mean PSNR > 36 dB). Across all classifiers, the Top-1 accuracy drop remains below 1.73 percentage points for DAC and 1.39 for PDCTC. Thus, DAC is promising for large-scale storage of JPEG images, offering stronger compression at the cost of a marginally larger classification accuracy drop compared to PDCTC.