Saliency-Aware Spatio-Temporal Artifact Detection for Compressed Video Quality Assessment

• URL: http://arxiv.org/abs/2301.01069v1
• Date: Tue, 3 Jan 2023 12:48:27 GMT
• Title: Saliency-Aware Spatio-Temporal Artifact Detection for Compressed Video Quality Assessment
• Authors: Liqun Lin, Yang Zheng, Weiling Chen, Chengdong Lan, Tiesong Zhao
• Abstract summary: Compressed videos often exhibit visually annoying artifacts, known as Perceivable Temporal Artifacts (PEAs) In this paper, we investigate the influence of four spatial PEAs (i.e. blurring, blocking, bleeding, and ringing) and two temporal PEAs (i.e. flickering and floating) on video quality. Based on the six types of PEAs, a quality metric called Saliency-Aware Spatio-Temporal Artifacts Measurement (SSTAM) is proposed.
• Score: 16.49357671290058
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Compressed videos often exhibit visually annoying artifacts, known as Perceivable Encoding Artifacts (PEAs), which dramatically degrade video visual quality. Subjective and objective measures capable of identifying and quantifying various types of PEAs are critical in improving visual quality. In this paper, we investigate the influence of four spatial PEAs (i.e. blurring, blocking, bleeding, and ringing) and two temporal PEAs (i.e. flickering and floating) on video quality. For spatial artifacts, we propose a visual saliency model with a low computational cost and higher consistency with human visual perception. In terms of temporal artifacts, self-attention based TimeSFormer is improved to detect temporal artifacts. Based on the six types of PEAs, a quality metric called Saliency-Aware Spatio-Temporal Artifacts Measurement (SSTAM) is proposed. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics. We believe that SSTAM will be beneficial for optimizing video coding techniques.

Related papers

• Fréchet Video Motion Distance: A Metric for Evaluating Motion Consistency in Videos [13.368981834953981]
  We propose Fr'echet Video Motion Distance metric, which focuses on evaluating motion consistency in video generation. Specifically, we design explicit motion features based on key point tracking, and then measure the similarity between these features via the Fr'echet distance. We carry out a large-scale human study, demonstrating that our metric effectively detects temporal noise and aligns better with human perceptions of generated video quality than existing metrics.
  arXiv  Detail & Related papers  (2024-07-23T02:10:50Z)
• MVAD: A Multiple Visual Artifact Detector for Video Streaming [7.782835693566871]
  Visual artifacts are often introduced into streamed video content, due to prevailing conditions during content production and delivery. Existing detection methods often focus on a single type of artifact and determine the presence of an artifact through thresholding objective quality indices. We propose a Multiple Visual Artifact Detector, MVAD, for video streaming which, for the first time, is able to detect multiple artifacts using a single framework.
  arXiv  Detail & Related papers  (2024-05-31T21:56:04Z)
• STREAM: Spatio-TempoRal Evaluation and Analysis Metric for Video Generative Models [6.855409699832414]
  Video generative models struggle to generate even short video clips. Current video evaluation metrics are simple adaptations of image metrics by switching the embeddings with video embedding networks. We propose STREAM, a new video evaluation metric uniquely designed to independently evaluate spatial and temporal aspects.
  arXiv  Detail & Related papers  (2024-01-30T08:18:20Z)
• ColorVideoVDP: A visual difference predictor for image, video and display distortions [51.29162719944865]
  metric is built on novel psychophysical models of chromatic contrast sensitivity and cross-channel contrast masking. It accounts for the viewing conditions, geometric, and photometric characteristics of the display. It was trained to predict common video streaming distortions and 8 new distortion types related to AR/VR displays.
  arXiv  Detail & Related papers  (2024-01-21T13:16:33Z)
• Geometry-Aware Video Quality Assessment for Dynamic Digital Human [56.17852258306602]
  We propose a novel no-reference (NR) geometry-aware video quality assessment method for DDH-QA challenge. The proposed method achieves state-of-the-art performance on the DDH-QA database.
  arXiv  Detail & Related papers  (2023-10-24T16:34:03Z)
• Exploring the Effectiveness of Video Perceptual Representation in Blind Video Quality Assessment [55.65173181828863]
  We propose a temporal perceptual quality index (TPQI) to measure the temporal distortion by describing the graphic morphology of the representation. Experiments show that TPQI is an effective way of predicting subjective temporal quality.
  arXiv  Detail & Related papers  (2022-07-08T07:30:51Z)
• DisCoVQA: Temporal Distortion-Content Transformers for Video Quality Assessment [56.42140467085586]
  Some temporal variations are causing temporal distortions and lead to extra quality degradations. Human visual system often has different attention to frames with different contents. We propose a novel and effective transformer-based VQA method to tackle these two issues.
  arXiv  Detail & Related papers  (2022-06-20T15:31:27Z)
• Video Salient Object Detection via Contrastive Features and Attention Modules [106.33219760012048]
  We propose a network with attention modules to learn contrastive features for video salient object detection. A co-attention formulation is utilized to combine the low-level and high-level features. We show that the proposed method requires less computation, and performs favorably against the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-11-03T17:40:32Z)
• Coherent Loss: A Generic Framework for Stable Video Segmentation [103.78087255807482]
  We investigate how a jittering artifact degrades the visual quality of video segmentation results. We propose a Coherent Loss with a generic framework to enhance the performance of a neural network against jittering artifacts.
  arXiv  Detail & Related papers  (2020-10-25T10:48:28Z)

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.