From Images to Point Clouds: An Efficient Solution for Cross-media Blind Quality Assessment without Annotated Training

• URL: http://arxiv.org/abs/2501.13387v1
• Date: Thu, 23 Jan 2025 05:15:10 GMT
• Title: From Images to Point Clouds: An Efficient Solution for Cross-media Blind Quality Assessment without Annotated Training
• Authors: Yipeng Liu, Qi Yang, Yujie Zhang, Yiling Xu, Le Yang, Zhu Li,
• Abstract summary: We present a novel quality assessment method which can predict the perceptual quality of point clouds from new scenes without available annotations. Recognizing the human visual system (HVS) as the decision-maker in quality assessment regardless of media types, we can emulate the evaluation criteria for human perception via neural networks. We propose the distortion-guided biased feature alignment which integrates existing/estimated distortion distribution into the adversarial DA framework.
• Score: 35.45364402708792
• License:
• Abstract: We present a novel quality assessment method which can predict the perceptual quality of point clouds from new scenes without available annotations by leveraging the rich prior knowledge in images, called the Distribution-Weighted Image-Transferred Point Cloud Quality Assessment (DWIT-PCQA). Recognizing the human visual system (HVS) as the decision-maker in quality assessment regardless of media types, we can emulate the evaluation criteria for human perception via neural networks and further transfer the capability of quality prediction from images to point clouds by leveraging the prior knowledge in the images. Specifically, domain adaptation (DA) can be leveraged to bridge the images and point clouds by aligning feature distributions of the two media in the same feature space. However, the different manifestations of distortions in images and point clouds make feature alignment a difficult task. To reduce the alignment difficulty and consider the different distortion distribution during alignment, we have derived formulas to decompose the optimization objective of the conventional DA into two suboptimization functions with distortion as a transition. Specifically, through network implementation, we propose the distortion-guided biased feature alignment which integrates existing/estimated distortion distribution into the adversarial DA framework, emphasizing common distortion patterns during feature alignment. Besides, we propose the quality-aware feature disentanglement to mitigate the destruction of the mapping from features to quality during alignment with biased distortions. Experimental results demonstrate that our proposed method exhibits reliable performance compared to general blind PCQA methods without needing point cloud annotations.

Related papers

• Image Quality Assessment: Enhancing Perceptual Exploration and Interpretation with Collaborative Feature Refinement and Hausdorff distance [47.01352278293561]
  Current full-reference image quality assessment (FR-IQA) methods often fuse features from reference and distorted images. This work introduces a pioneering training-free FR-IQA method that accurately predicts image quality in alignment with the human visual system.
  arXiv  Detail & Related papers  (2024-12-20T12:39:49Z)
• Contrastive Pre-Training with Multi-View Fusion for No-Reference Point Cloud Quality Assessment [49.36799270585947]
  No-reference point cloud quality assessment (NR-PCQA) aims to automatically evaluate the perceptual quality of distorted point clouds without available reference. We propose a novel contrastive pre-training framework tailored for PCQA (CoPA) Our method outperforms the state-of-the-art PCQA methods on popular benchmarks.
  arXiv  Detail & Related papers  (2024-03-15T07:16:07Z)
• PAME: Self-Supervised Masked Autoencoder for No-Reference Point Cloud Quality Assessment [34.256276774430575]
  No-reference point cloud quality assessment (NR-PCQA) aims to automatically predict the perceptual quality of point clouds without reference. We propose a self-supervised pre-training framework using masked autoencoders (PAME) to help the model learn useful representations without labels. Our method outperforms the state-of-the-art NR-PCQA methods on popular benchmarks in terms of prediction accuracy and generalizability.
  arXiv  Detail & Related papers  (2024-03-15T07:01:33Z)
• Adaptive Feature Selection for No-Reference Image Quality Assessment by Mitigating Semantic Noise Sensitivity [55.399230250413986]
  We propose a Quality-Aware Feature Matching IQA Metric (QFM-IQM) to remove harmful semantic noise features from the upstream task. Our approach achieves superior performance to the state-of-the-art NR-IQA methods on eight standard IQA datasets.
  arXiv  Detail & Related papers  (2023-12-11T06:50:27Z)
• Simple Baselines for Projection-based Full-reference and No-reference Point Cloud Quality Assessment [60.2709006613171]
  We propose simple baselines for projection-based point cloud quality assessment (PCQA) We use multi-projections obtained via a common cube-like projection process from the point clouds for both full-reference (FR) and no-reference (NR) PCQA tasks. Taking part in the ICIP 2023 PCVQA Challenge, we succeeded in achieving the top spot in four out of the five competition tracks.
  arXiv  Detail & Related papers  (2023-10-26T04:42:57Z)
• Reduced-Reference Quality Assessment of Point Clouds via Content-Oriented Saliency Projection [17.983188216548005]
  Many dense 3D point clouds have been exploited to represent visual objects instead of traditional images or videos. We propose a novel and efficient Reduced-Reference quality metric for point clouds.
  arXiv  Detail & Related papers  (2023-01-18T18:00:29Z)
• 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)
• No-Reference Image Quality Assessment by Hallucinating Pristine Features [24.35220427707458]
  We propose a no-reference (NR) image quality assessment (IQA) method via feature level pseudo-reference (PR) hallucination. The effectiveness of our proposed method is demonstrated on four popular IQA databases.
  arXiv  Detail & Related papers  (2021-08-09T16:48:34Z)
• Unpaired Image Enhancement with Quality-Attention Generative Adversarial Network [92.01145655155374]
  We propose a quality attention generative adversarial network (QAGAN) trained on unpaired data. Key novelty of the proposed QAGAN lies in the injected QAM for the generator. Our proposed method achieves better performance in both objective and subjective evaluations.
  arXiv  Detail & Related papers  (2020-12-30T05:57:20Z)

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.