Understanding How Image Quality Affects Deep Neural Networks

TL;DR

This paper evaluates how image quality distortions like blur, noise, and compression affect the performance of state-of-the-art deep neural networks in image classification tasks.

Contribution

It provides a systematic assessment of the robustness of leading neural networks against various image quality distortions, highlighting their vulnerabilities.

Findings

Networks are sensitive to blur and noise distortions.

Performance degrades significantly with JPEG and JPEG2000 compression.

Results suggest the need for more invariant neural network architectures.

Abstract

Image quality is an important practical challenge that is often overlooked in the design of machine vision systems. Commonly, machine vision systems are trained and tested on high quality image datasets, yet in practical applications the input images can not be assumed to be of high quality. Recently, deep neural networks have obtained state-of-the-art performance on many machine vision tasks. In this paper we provide an evaluation of 4 state-of-the-art deep neural network models for image classification under quality distortions. We consider five types of quality distortions: blur, noise, contrast, JPEG, and JPEG2000 compression. We show that the existing networks are susceptible to these quality distortions, particularly to blur and noise. These results enable future work in developing deep neural networks that are more invariant to quality distortions.