Adacompress: Adaptive compression for online computer vision services

Presented by

Ahmed Salamah

Introduction

One of the major parameters that can be changed in the JPEG pipeline is the quantization table, which is the main source of artifacts to be added in the image to make it lossless compression. The authors got motivated to change the JPEG configuration to optimize the uploading rate of different cloud computer vision without considering pre-knowledge of the original model and dataset. In contrast to other papers in the literature which they adjust the JPEG configuration according to retrain the parameters or the structure of the model. They considered the lack of undefining the quantization level which decreases the image rate and quality but the deep learning model can still recognize it. The authors used Deep Reinforcement learning (DRL) in an online manner to choose the quantization level to upload an image to the cloud for the computer vision model and this is the only approach to design an adaptive JPEG based on RL mechanism.

The approach is designed based on an interactive training environment which represents any computer vision cloud services, then they needed a tool to evaluate and predict the performance of quantization level on an uploaded image, so they used a deep Q neural network agent. They feed the agent with a reward function which considers two optimization parameters, which are accuracy and image size, that work with iterative behavior interacting with the environment. The environment is exposed to different images with different virtual redundant information that needs an adaptive solution for each image to select the suitable compression level for the model. Thus, they designed an explore-exploit mechanism to train the agent on different scenery which is designed in deep Q agent as an inference-estimate-retain mechanism to control to restart the training procedure for each image. The authors verify their approach by providing some analysis and insight using Grad-Cam by showing some patterns of each image with its own corresponding quality factor. Each image shows a different response from a deep model to show that images are more sensitive to large smooth areas, while is more robust compression for images with complex textures.