On the Impact of Lossy Image and Video Compression on the Performance of Deep Convolutional Neural Network Architectures

TL;DR

This study investigates how common lossy image and video compression techniques like JPEG and H.264 affect the performance of various deep CNN architectures across multiple image understanding tasks, revealing non-linear impacts and potential for performance recovery through retraining.

Contribution

It provides a comprehensive analysis of the impact of JPEG and H.264 compression on diverse CNN architectures and tasks, highlighting the potential for retraining to mitigate performance loss.

Findings

Performance drops sharply below JPEG quality 15% and H.264 CRF 40.

Retraining on compressed images can recover up to 78.4% of performance.

Encoder-decoder architectures tend to be more resilient to compression effects.

Abstract

Recent advances in generalized image understanding have seen a surge in the use of deep convolutional neural networks (CNN) across a broad range of image-based detection, classification and prediction tasks. Whilst the reported performance of these approaches is impressive, this study investigates the hitherto unapproached question of the impact of commonplace image and video compression techniques on the performance of such deep learning architectures. Focusing on the JPEG and H.264 (MPEG-4 AVC) as a representative proxy for contemporary lossy image/video compression techniques that are in common use within network-connected image/video devices and infrastructure, we examine the impact on performance across five discrete tasks: human pose estimation, semantic segmentation, object detection, action recognition, and monocular depth estimation. As such, within this study we include a variety of network architectures and domains spanning end-to-end convolution, encoder-decoder, region-based CNN (R-CNN), dual-stream, and generative adversarial networks (GAN). Our results show a non-linear and non-uniform relationship between network performance and the level of lossy compression applied. Notably, performance decreases significantly below a JPEG quality (quantization) level of 15% and a H.264 Constant Rate Factor (CRF) of 40. However, retraining said architectures on pre-compressed imagery conversely recovers network performance by up to 78.4% in some cases. Furthermore, there is a correlation between architectures employing an encoder-decoder pipeline and those that demonstrate resilience to lossy image compression. The characteristics of the relationship between input compression to output task performance can be used to inform design decisions within future image/video devices and infrastructure.