Reduced Reference Perceptual Quality Model and Application to Rate Control for 3D Point Cloud Compression

• URL: http://arxiv.org/abs/2011.12688v1
• Date: Wed, 25 Nov 2020 12:42:02 GMT
• Title: Reduced Reference Perceptual Quality Model and Application to Rate Control for 3D Point Cloud Compression
• Authors: Qi Liu, Hui Yuan, Raouf Hamzaoui, Honglei Su, Junhui Hou, Huan Yang
• Abstract summary: In rate-distortion optimization, the encoder settings are determined by maximizing a reconstruction quality measure subject to a constraint on the bit rate. We propose a linear perceptual quality model whose variables are the V-PCC geometry and color quantization parameters. Subjective quality tests with 400 compressed 3D point clouds show that the proposed model correlates well with the mean opinion score. We show that for the same target bit rate, ratedistortion optimization based on the proposed model offers higher perceptual quality than rate-distortion optimization based on exhaustive search with a point-to-point objective quality metric.
• Score: 61.110938359555895
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In rate-distortion optimization, the encoder settings are determined by maximizing a reconstruction quality measure subject to a constraint on the bit rate. One of the main challenges of this approach is to define a quality measure that can be computed with low computational cost and which correlates well with perceptual quality. While several quality measures that fulfil these two criteria have been developed for images and video, no such one exists for 3D point clouds. We address this limitation for the video-based point cloud compression (V-PCC) standard by proposing a linear perceptual quality model whose variables are the V-PCC geometry and color quantization parameters and whose coefficients can easily be computed from two features extracted from the original 3D point cloud. Subjective quality tests with 400 compressed 3D point clouds show that the proposed model correlates well with the mean opinion score, outperforming state-of-the-art full reference objective measures in terms of Spearman rank-order and Pearsons linear correlation coefficient. Moreover, we show that for the same target bit rate, ratedistortion optimization based on the proposed model offers higher perceptual quality than rate-distortion optimization based on exhaustive search with a point-to-point objective quality metric.

Related papers

• Full reference point cloud quality assessment using support vector regression [27.462663794942756]
  This paper presents an accurate full-reference point cloud quality assessment (FR-PCQA) method called full-reference quality assessment using support vector regression (FRSVR) The proposed method achieves a superior trade-off between accuracy and calculation speed because it includes only the calculation of these five simple metrics and SVR. Experimental results with three types of open datasets show that the proposed method is more accurate than conventional FR-PCQA methods.
  arXiv  Detail & Related papers  (2024-06-15T06:22:26Z)
• 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)
• Activating Frequency and ViT for 3D Point Cloud Quality Assessment without Reference [0.49157446832511503]
  We propose no-reference quality metric of a given 3D-PC. To map the input attributes to quality score, we use a light-weight hybrid deep model; combined of Deformable Convolutional Network (DCN) and Vision Transformers (ViT) The results show that our approach outperforms state-of-the-art NR-PCQA measures and even some FR-PCQA on PointXR.
  arXiv  Detail & Related papers  (2023-12-10T19:13:34Z)
• 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)
• Grad-PU: Arbitrary-Scale Point Cloud Upsampling via Gradient Descent with Learned Distance Functions [77.32043242988738]
  We propose a new framework for accurate point cloud upsampling that supports arbitrary upsampling rates. Our method first interpolates the low-res point cloud according to a given upsampling rate.
  arXiv  Detail & Related papers  (2023-04-24T06:36:35Z)
• 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)
• TCDM: Transformational Complexity Based Distortion Metric for Perceptual Point Cloud Quality Assessment [24.936061591860838]
  The goal of objective point cloud quality assessment (PCQA) research is to develop metrics that measure point cloud quality in a consistent manner. We evaluate the point cloud quality by measuring the complexity of transforming the distorted point cloud back to its reference. The effectiveness of the proposed transformational complexity based distortion metric (TCDM) is evaluated through extensive experiments conducted on five public point cloud quality assessment databases.
  arXiv  Detail & Related papers  (2022-10-10T13:20:51Z)
• Blind Quality Assessment of 3D Dense Point Clouds with Structure Guided Resampling [71.68672977990403]
  We propose an objective point cloud quality index with Structure Guided Resampling (SGR) to automatically evaluate the perceptually visual quality of 3D dense point clouds. The proposed SGR is a general-purpose blind quality assessment method without the assistance of any reference information.
  arXiv  Detail & Related papers  (2022-08-31T02:42:55Z)
• Evaluating Point Cloud from Moving Camera Videos: A No-Reference Metric [58.309735075960745]
  This paper explores the way of dealing with point cloud quality assessment (PCQA) tasks via video quality assessment (VQA) methods. We generate the captured videos by rotating the camera around the point clouds through several circular pathways. We extract both spatial and temporal quality-aware features from the selected key frames and the video clips through using trainable 2D-CNN and pre-trained 3D-CNN models.
  arXiv  Detail & Related papers  (2022-08-30T08:59:41Z)
• A No-reference Quality Assessment Metric for Point Cloud Based on Captured Video Sequences [40.46566408312466]
  We propose a no-reference quality assessment metric for colored point cloud based on captured video sequences. The experimental results show that our method outperforms most of the state-of-the-art full-reference and no-reference PCQA metrics.
  arXiv  Detail & Related papers  (2022-06-09T06:42:41Z)

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.