S-Net: A Scalable Convolutional Neural Network for JPEG Compression Artifact Reduction

TL;DR

This paper introduces S-Net, a scalable CNN architecture for JPEG artifact reduction that dynamically adjusts its scale for real-time processing, outperforming existing CNN methods on multiple datasets.

Contribution

The paper presents a novel scalable CNN architecture, S-Net, enabling dynamic network scaling for efficient JPEG artifact removal with minimal performance loss.

Findings

Outperforms existing CNN-based methods on benchmark datasets

Achieves state-of-the-art performance in JPEG artifact reduction

Demonstrates effective network scale adjustment for real-time operation

Abstract

Recent studies have used deep residual convolutional neural networks (CNNs) for JPEG compression artifact reduction. This study proposes a scalable CNN called S-Net. Our approach effectively adjusts the network scale dynamically in a multitask system for real-time operation with little performance loss. It offers a simple and direct technique to evaluate the performance gains obtained with increasing network depth, and it is helpful for removing redundant network layers to maximize the network efficiency. We implement our architecture using the Keras framework with the TensorFlow backend on an NVIDIA K80 GPU server. We train our models on the DIV2K dataset and evaluate their performance on public benchmark datasets. To validate the generality and universality of the proposed method, we created and utilized a new dataset, called WIN143, for over-processed images evaluation. Experimental results indicate that our proposed approach outperforms other CNN-based methods and achieves state-of-the-art performance.