Towards Flexible Blind JPEG Artifacts Removal

• URL: http://arxiv.org/abs/2109.14573v1
• Date: Wed, 29 Sep 2021 17:12:10 GMT
• Title: Towards Flexible Blind JPEG Artifacts Removal
• Authors: Jiaxi Jiang, Kai Zhang, Radu Timofte
• Abstract summary: We propose a flexible blind convolutional neural network, namely FBCNN, that can predict the adjustable quality factor to control the trade-off between artifacts removal and details preservation. Our proposed FBCNN achieves favorable performance against state-of-the-art methods in terms of both quantitative metrics and visual quality.
• Score: 73.46374658847675
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Training a single deep blind model to handle different quality factors for JPEG image artifacts removal has been attracting considerable attention due to its convenience for practical usage. However, existing deep blind methods usually directly reconstruct the image without predicting the quality factor, thus lacking the flexibility to control the output as the non-blind methods. To remedy this problem, in this paper, we propose a flexible blind convolutional neural network, namely FBCNN, that can predict the adjustable quality factor to control the trade-off between artifacts removal and details preservation. Specifically, FBCNN decouples the quality factor from the JPEG image via a decoupler module and then embeds the predicted quality factor into the subsequent reconstructor module through a quality factor attention block for flexible control. Besides, we find existing methods are prone to fail on non-aligned double JPEG images even with only a one-pixel shift, and we thus propose a double JPEG degradation model to augment the training data. Extensive experiments on single JPEG images, more general double JPEG images, and real-world JPEG images demonstrate that our proposed FBCNN achieves favorable performance against state-of-the-art methods in terms of both quantitative metrics and visual quality.

Related papers

• JPEG Inspired Deep Learning [4.958744940097937]
  Well-crafted JPEG compression can actually improve the performance of deep learning (DL) We propose JPEG-DL, a novel DL framework that prepends any underlying DNN architecture with a trainable JPEG compression layer.
  arXiv  Detail & Related papers  (2024-10-09T17:23:54Z)
• Lightweight Adaptive Feature De-drifting for Compressed Image Classification [10.265991649449507]
  High-level vision models trained on high-quality images will suffer performance degradation when dealing with compressed images. Various learning-based JPEG artifact removal methods have been proposed to handle visual artifacts. This paper proposes a novel lightweight AFD module to boost the performance of pre-trained image classification models when facing compressed images.
  arXiv  Detail & Related papers  (2024-01-03T13:03:44Z)
• Learned Lossless Compression for JPEG via Frequency-Domain Prediction [50.20577108662153]
  We propose a novel framework for learned lossless compression of JPEG images. To enable learning in the frequency domain, DCT coefficients are partitioned into groups to utilize implicit local redundancy. An autoencoder-like architecture is designed based on the weight-shared blocks to realize entropy modeling of grouped DCT coefficients.
  arXiv  Detail & Related papers  (2023-03-05T13:15:28Z)
• High-Perceptual Quality JPEG Decoding via Posterior Sampling [13.238373528922194]
  We propose a different paradigm for JPEG artifact correction. We aim to obtain sharp, detailed and visually reconstructed images, while being consistent with the compressed input. Our solution offers a diverse set of plausible and fast reconstructions for a given input with perfect consistency.
  arXiv  Detail & Related papers  (2022-11-21T19:47:59Z)
• Practical Learned Lossless JPEG Recompression with Multi-Level Cross-Channel Entropy Model in the DCT Domain [10.655855413391324]
  We propose a deep learning based JPEG recompression method that operates on DCT domain. Experiments show that our method achieves state-of-the-art performance compared with traditional JPEG recompression methods.
  arXiv  Detail & Related papers  (2022-03-30T14:36:13Z)
• Palette: Image-to-Image Diffusion Models [50.268441533631176]
  We introduce Palette, a simple and general framework for image-to-image translation using conditional diffusion models. On four challenging image-to-image translation tasks, Palette outperforms strong GAN and regression baselines. We report several sample quality scores including FID, Inception Score, Classification Accuracy of a pre-trained ResNet-50, and Perceptual Distance against reference images.
  arXiv  Detail & Related papers  (2021-11-10T17:49:29Z)
• Improving Cost Learning for JPEG Steganography by Exploiting JPEG Domain Knowledge [52.880912557105106]
  JEC-RL is explicitly designed to tailor the JPEG DCT structure. It works with the embedding action sampling mechanism under reinforcement learning. It can achieve good security performance for JPEG images against both advanced feature based and modern CNN based steganalyzers.
  arXiv  Detail & Related papers  (2021-05-09T08:10:41Z)
• Towards Unsupervised Deep Image Enhancement with Generative Adversarial Network [92.01145655155374]
  We present an unsupervised image enhancement generative network (UEGAN) It learns the corresponding image-to-image mapping from a set of images with desired characteristics in an unsupervised manner. Results show that the proposed model effectively improves the aesthetic quality of images.
  arXiv  Detail & Related papers  (2020-12-30T03:22:46Z)
• Learning a Single Model with a Wide Range of Quality Factors for JPEG Image Artifacts Removal [24.25688335628976]
  Lossy compression brings artifacts into the compressed image and degrades the visual quality. In this paper, we propose a highly robust compression artifacts removal network. Our proposed network is a single model approach that can be trained for handling a wide range of quality factors.
  arXiv  Detail & Related papers  (2020-09-15T08:16:58Z)
• Quantization Guided JPEG Artifact Correction [69.04777875711646]
  We develop a novel architecture for artifact correction using the JPEG files quantization matrix. This allows our single model to achieve state-of-the-art performance over models trained for specific quality settings.
  arXiv  Detail & Related papers  (2020-04-17T00:10:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.