Joint Quality Assessment and Example-Guided Image Processing by Disentangling Picture Appearance from Content

• URL: http://arxiv.org/abs/2404.13484v1
• Date: Sat, 20 Apr 2024 23:02:57 GMT
• Title: Joint Quality Assessment and Example-Guided Image Processing by Disentangling Picture Appearance from Content
• Authors: Abhinau K. Venkataramanan, Cosmin Stejerean, Ioannis Katsavounidis, Hassene Tmar, Alan C. Bovik,
• Abstract summary: Deep learning has impacted low-level image processing tasks such as style/domain transfer, enhancement/restoration, and visual quality assessments. We leverage this observation to develop a novel disentangled representation learning method that decomposes inputs into content and appearance features. We demonstrate through extensive evaluations that DisQUE achieves accuracy across quality prediction tasks and distortion types.
• Score: 30.939589712281684
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The deep learning revolution has strongly impacted low-level image processing tasks such as style/domain transfer, enhancement/restoration, and visual quality assessments. Despite often being treated separately, the aforementioned tasks share a common theme of understanding, editing, or enhancing the appearance of input images without modifying the underlying content. We leverage this observation to develop a novel disentangled representation learning method that decomposes inputs into content and appearance features. The model is trained in a self-supervised manner and we use the learned features to develop a new quality prediction model named DisQUE. We demonstrate through extensive evaluations that DisQUE achieves state-of-the-art accuracy across quality prediction tasks and distortion types. Moreover, we demonstrate that the same features may also be used for image processing tasks such as HDR tone mapping, where the desired output characteristics may be tuned using example input-output pairs.

Related papers

• ExIQA: Explainable Image Quality Assessment Using Distortion Attributes [0.3683202928838613]
  We propose an explainable approach for distortion identification based on attribute learning. We generate a dataset consisting of 100,000 images for efficient training. Our approach achieves state-of-the-art (SOTA) performance across multiple datasets in both PLCC and SRCC metrics.
  arXiv  Detail & Related papers  (2024-09-10T20:28:14Z)
• Vision Language Modeling of Content, Distortion and Appearance for Image Quality Assessment [20.851102845794244]
  Distilling high level knowledge about quality bearing attributes is crucial for developing objective Image Quality Assessment (IQA) We present a new blind IQA (BIQA) model termed Self-supervision and Vision-Language supervision Image QUality Evaluator (SLIQUE) SLIQUE features a joint vision-language and visual contrastive representation learning framework for acquiring high level knowledge about the images semantic contents, distortion characteristics and appearance properties for IQA.
  arXiv  Detail & Related papers  (2024-06-14T09:18:28Z)
• Dual-Branch Network for Portrait Image Quality Assessment [76.27716058987251]
  We introduce a dual-branch network for portrait image quality assessment (PIQA) We utilize two backbone networks (textiti.e., Swin Transformer-B) to extract the quality-aware features from the entire portrait image and the facial image cropped from it. We leverage LIQE, an image scene classification and quality assessment model, to capture the quality-aware and scene-specific features as the auxiliary features.
  arXiv  Detail & Related papers  (2024-05-14T12:43:43Z)
• Let's ViCE! Mimicking Human Cognitive Behavior in Image Generation Evaluation [96.74302670358145]
  We introduce an automated method for Visual Concept Evaluation (ViCE) to assess consistency between a generated/edited image and the corresponding prompt/instructions. ViCE combines the strengths of Large Language Models (LLMs) and Visual Question Answering (VQA) into a unified pipeline, aiming to replicate the human cognitive process in quality assessment.
  arXiv  Detail & Related papers  (2023-07-18T16:33:30Z)
• Blind Image Quality Assessment via Transformer Predicted Error Map and Perceptual Quality Token [19.67014524146261]
  No-reference image quality assessment (NR-IQA) has gained increasing attention recently. We propose a Transformer based NR-IQA model using a predicted objective error map and perceptual quality token. Our proposed method outperforms the current state-of-the-art in both authentic and synthetic image databases.
  arXiv  Detail & Related papers  (2023-05-16T11:17:54Z)
• Test your samples jointly: Pseudo-reference for image quality evaluation [3.2634122554914]
  We propose to jointly model different images depicting the same content to improve the precision of quality estimation. Our experiments show that at test-time, our method successfully combines the features from multiple images depicting the same new content, improving estimation quality.
  arXiv  Detail & Related papers  (2023-04-07T17:59:27Z)
• Images Speak in Images: A Generalist Painter for In-Context Visual Learning [98.78475432114595]
  In-context learning allows the model to rapidly adapt to various tasks with only a handful of prompts and examples. It is unclear how to define the general-purpose task prompts that the vision model can understand and transfer to out-of-domain tasks. We present Painter, a generalist model which redefines the output of core vision tasks as images, and specify task prompts as also images.
  arXiv  Detail & Related papers  (2022-12-05T18:59:50Z)
• ExCon: Explanation-driven Supervised Contrastive Learning for Image Classification [12.109442912963969]
  We propose to leverage saliency-based explanation methods to create content-preserving masked augmentations for contrastive learning. Our novel explanation-driven supervised contrastive learning (ExCon) methodology critically serves the dual goals of encouraging nearby image embeddings to have similar content and explanation. We demonstrate that ExCon outperforms vanilla supervised contrastive learning in terms of classification, explanation quality, adversarial robustness as well as calibration of probabilistic predictions of the model in the context of distributional shift.
  arXiv  Detail & Related papers  (2021-11-28T23:15:26Z)
• Image Quality Assessment using Contrastive Learning [50.265638572116984]
  We train a deep Convolutional Neural Network (CNN) using a contrastive pairwise objective to solve the auxiliary problem. We show through extensive experiments that CONTRIQUE achieves competitive performance when compared to state-of-the-art NR image quality models. Our results suggest that powerful quality representations with perceptual relevance can be obtained without requiring large labeled subjective image quality datasets.
  arXiv  Detail & Related papers  (2021-10-25T21:01:00Z)
• 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)
• Generative Hierarchical Features from Synthesizing Images [65.66756821069124]
  We show that learning to synthesize images can bring remarkable hierarchical visual features that are generalizable across a wide range of applications. The visual feature produced by our encoder, termed as Generative Hierarchical Feature (GH-Feat), has strong transferability to both generative and discriminative tasks.
  arXiv  Detail & Related papers  (2020-07-20T18:04:14Z)

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.