2C-Net: integrate image compression and classification via deep neural network

Abstract

Providing effective support for intelligent vision tasks without image reconstruction can save numerous computational costs in the era of big data. With the help of the Deep Neural Network (DNN), integrating image compression and intelligent vision tasks at a feature representation level becomes a new promising approach. But how to perform non-linear transformation for image compression and extract image patterns for intelligent vision tasks simultaneously within a shared DNN remains an open problem. In this paper, a versatile framework is studied to explore the common feature representations for both image compression and classification. A fully-shared latent representation is extracted in a more compact way to support compression and classification task. The General Feature Extraction and Feature-analytic Classifier are proposed to generate and utilize compact and general shared latent representation. Then the whole framework is joint optimized by considering multiple factors (i.e., rate, quality and accuracy). Extensive experiments are carried out to validate that the proposals can improve the performance of both learning-based image compression and classification.The results show that the proposed method outperforms conventional codecs like BPG and JPEG2000 in compression efficiency, while achieving acceptable accuracy on different datasets without image reconstruction.

Conditional Encoder-Based Adaptive Deep Image Compression with Classification-Driven Semantic Awareness

“…The hybrid contexts DIC is designed to optimize both image reconstruction and specific high-level tasks, while catering to both human and machine perception. This DIC can be classified into two types: compression feature-based and reconstruction image-based hybrid contexts DIC, depending on the input at the receiver performing the computer vision task [4,[32][33][34][35][36]. In this paper, we focus on the latter, as it only requires one image reconstruction network to achieve both high-quality reconstructed images and accurate machine task results, while the former needs multiple decoders for different machine tasks.…”