DeepN-JPEG: A Deep Neural Network Favorable JPEG-based Image Compression Framework

The marriage of big data and deep learning leads to the great success of artificial intelligence, but it also raises new challenges in data communication, storage and computation [7] incurred by the growing amount of distributed data and the increasing DNN model size. For resource-constrained IoT applications, while recent researches have been conducted [8, 9] to handle the computation and memory-intensive DNN workloads in an energy efficient manner, there lack efficient solutions to reduce the power-hungry data offloading and storage on terminal devices like edge sensors, especially in face of the stringent constraints on communication bandwidth, energy and hardware resources. Recent studies show that the latencies to upload a JPEG-compressed input image (i.e. 152KB) for a single inference of a popular CNN–“AlexNet” via stable wireless connections with 3G (870ms), LTE (180ms) and Wi-Fi (95ms), can exceed that of DNN computation (6∼82ms) by a mobile or cloud-GPU [10]. Moreover, the communication energy is comparable with the associated DNN computation energy.

Existing image compression frameworks (such as JPEG) can compress data aggressively, but they are often optimized for the Human-Visual System (HVS) or human’s perceived image quality, which can lead to unacceptable DNN accuracy degradation at higher compression ratios (CR) and thus significantly harm the quality of intelligent services. As shown later, testing a well-trained AlexNet using CR =∼ 5× compressed JPEG images (w.r.t. CR = 1× high quality images ), can lead to ∼ 9% image recognition accuracy reduction for the large scale dataset— ImageNet, almost offsetting the improvement brought by more complex DNN topology, i.e. from AlexNet to GoogLeNet (8 layers, 724M MACs v.s. 22 layers, 1.43G MACs) [11, 12]. This prompts the need of developing an DNN-favorable deep compression framework.

DeepN-JPEG: A Deep Neural Network Favorable JPEG-based Image Compression Framework
https://arxiv.org/pdf/1803.05788.pdf


Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples
https://arxiv.org/pdf/1803.05787.pdf