Understanding How Image Quality Affects Deep Neural Networks (1604.04004v2)

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.

• The paper demonstrates that common distortions, especially blur and noise, sharply degrade the performance of deep neural networks.
• It analyzes four DNN architectures across various distortions using a subset of the ImageNet dataset to quantify performance degradation.
• The study highlights the need for robust network design and augmented training methods to counteract quality impairments in real-world applications.

Understanding How Image Quality Affects Deep Neural Networks

The paper by Samuel Dodge and Lina Karam presents an in-depth examination of the susceptibility of deep neural networks (DNNs) to quality distortions in image data, a topic of importance given the reliance of machine vision systems on high-quality imagery. This research is pivotal for advancing our understanding of how DNNs can be enhanced to maintain performance despite variations in input quality.

Overview

This work evaluates four state-of-the-art DNN models: Caffe Reference, VGG-CNN-S, VGG16, and GoogleNet, focusing on their performance under five types of image distortions—blur, noise, contrast, JPEG, and JPEG2000 compression. The paper uses a subset of the ImageNet ILSVRC 2012 dataset, specifically selecting 10,000 images to systematically apply various levels of each distortion type. The primary goal is to assess and quantify the performance degradation of these models under non-standard conditions.

Key Findings

The findings indicate a notable vulnerability to blur and noise. While DNNs are robust to JPEG and JPEG2000 compression as well as contrast changes at typical quality levels, the performance sharply declines with increased blur and noise. Specifically, the VGG16 network demonstrates superior resilience and accuracy across all distortions compared to other tested architectures.

The research highlights the need for DNNs that can maintain efficacy despite common real-world quality imperfections. It suggests that current DNN architectures, regardless of depth or complexity, share this limitation, pointing to an architectural or methodological gap.

Implications and Future Directions

Practically, these findings emphasize the importance of designing more robust neural network architectures capable of handling variations in image quality. This is especially relevant in applications like surveillance and mobile vision systems, where high-quality inputs cannot always be guaranteed. Additionally, the need to explore training with augmented datasets that include various distortions is suggested, though caution is warranted regarding potential trade-offs with performance on high-quality images.

Theoretically, this points to potential research directions focusing on either adaptation of existing network architectures or creation of novel architectures. This could include approaches to incorporate invariance to specific distortions through enhanced model design or preprocessing strategies.

Looking forward, this paper sets the stage for future research to explore hybrid models that integrate ideas from robust signal processing and deep learning, possibly leveraging domain adaptation techniques or transfer learning to achieve more generalized invariant characteristics.

In conclusion, Dodge and Karam’s paper underlines the necessity for continued examination into the adaptability of DNNs, presenting a foundational analysis that will inform future innovations in creating resilient machine vision systems.