TINQ: Temporal Inconsistency Guided Blind Video Quality Assessment

• URL: http://arxiv.org/abs/2412.18933v1
• Date: Wed, 25 Dec 2024 15:43:41 GMT
• Title: TINQ: Temporal Inconsistency Guided Blind Video Quality Assessment
• Authors: Yixiao Li, Xiaoyuan Yang, Weide Liu, Xin Jin, Xu Jia, Yukun Lai, Haotao Liu, Paul L Rosin, Wei Zhou,
• Abstract summary: Blind video quality assessment (BVQA) has been actively researched for user-generated content (UGC) videos. Recent super-resolution (SR) techniques have been widely applied in videos. Temporal inconsistency, referring to irregularities between consecutive frames, is relevant to video quality.
• Score: 61.76431477117295
• License:
• Abstract: Blind video quality assessment (BVQA) has been actively researched for user-generated content (UGC) videos. Recently, super-resolution (SR) techniques have been widely applied in UGC. Therefore, an effective BVQA method for both UGC and SR scenarios is essential. Temporal inconsistency, referring to irregularities between consecutive frames, is relevant to video quality. Current BVQA approaches typically model temporal relationships in UGC videos using statistics of motion information, but inconsistencies remain unexplored. Additionally, different from temporal inconsistency in UGC videos, such inconsistency in SR videos is amplified due to upscaling algorithms. In this paper, we introduce the Temporal Inconsistency Guided Blind Video Quality Assessment (TINQ) metric, demonstrating that exploring temporal inconsistency is crucial for effective BVQA. Since temporal inconsistencies vary between UGC and SR videos, they are calculated in different ways. Based on this, a spatial module highlights inconsistent areas across consecutive frames at coarse and fine granularities. In addition, a temporal module aggregates features over time in two stages. The first stage employs a visual memory capacity block to adaptively segment the time dimension based on estimated complexity, while the second stage focuses on selecting key features. The stages work together through Consistency-aware Fusion Units to regress cross-time-scale video quality. Extensive experiments on UGC and SR video quality datasets show that our method outperforms existing state-of-the-art BVQA methods. Code is available at https://github.com/Lighting-YXLI/TINQ.

Related papers

• Temporally Consistent Referring Video Object Segmentation with Hybrid Memory [98.80249255577304]
  We propose an end-to-end R-VOS paradigm that explicitly models temporal consistency alongside the referring segmentation. Features of frames with automatically generated high-quality reference masks are propagated to segment remaining frames. Extensive experiments demonstrate that our approach enhances temporal consistency by a significant margin.
  arXiv  Detail & Related papers  (2024-03-28T13:32:49Z)
• Capturing Co-existing Distortions in User-Generated Content for No-reference Video Quality Assessment [9.883856205077022]
  Video Quality Assessment (VQA) aims to predict the perceptual quality of a video. VQA faces two under-estimated challenges unresolved in User Generated Content (UGC) videos. We propose textitVisual Quality Transformer (VQT) to extract quality-related sparse features more efficiently.
  arXiv  Detail & Related papers  (2023-07-31T16:29:29Z)
• Transform-Equivariant Consistency Learning for Temporal Sentence Grounding [66.10949751429781]
  We introduce a novel Equivariant Consistency Regulation Learning framework to learn more discriminative representations for each video. Our motivation comes from that the temporal boundary of the query-guided activity should be consistently predicted. In particular, we devise a self-supervised consistency loss module to enhance the completeness and smoothness of the augmented video.
  arXiv  Detail & Related papers  (2023-05-06T19:29:28Z)
• 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)
• ST-GREED: Space-Time Generalized Entropic Differences for Frame Rate Dependent Video Quality Prediction [63.749184706461826]
  We study how perceptual quality is affected by frame rate, and how frame rate and compression combine to affect perceived quality. We devise an objective VQA model called Space-Time GeneRalized Entropic Difference (GREED) which analyzes the statistics of spatial and temporal band-pass video coefficients. GREED achieves state-of-the-art performance on the LIVE-YT-HFR Database when compared with existing VQA models.
  arXiv  Detail & Related papers  (2020-10-26T16:54:33Z)
• Temporal Context Aggregation for Video Retrieval with Contrastive Learning [81.12514007044456]
  We propose TCA, a video representation learning framework that incorporates long-range temporal information between frame-level features. The proposed method shows a significant performance advantage (17% mAP on FIVR-200K) over state-of-the-art methods with video-level features.
  arXiv  Detail & Related papers  (2020-08-04T05:24:20Z)

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.