Perceptual Video Quality Assessment: A Survey
- URL: http://arxiv.org/abs/2402.03413v1
- Date: Mon, 5 Feb 2024 16:13:52 GMT
- Title: Perceptual Video Quality Assessment: A Survey
- Authors: Xiongkuo Min, Huiyu Duan, Wei Sun, Yucheng Zhu, Guangtao Zhai
- Abstract summary: Perceptual video quality assessment plays a vital role in the field of video processing.
Various subjective and objective video quality assessment studies have been conducted over the past two decades.
This survey provides an up-to-date and comprehensive review of these video quality assessment studies.
- Score: 63.61214597655413
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Perceptual video quality assessment plays a vital role in the field of video
processing due to the existence of quality degradations introduced in various
stages of video signal acquisition, compression, transmission and display. With
the advancement of internet communication and cloud service technology, video
content and traffic are growing exponentially, which further emphasizes the
requirement for accurate and rapid assessment of video quality. Therefore,
numerous subjective and objective video quality assessment studies have been
conducted over the past two decades for both generic videos and specific videos
such as streaming, user-generated content (UGC), 3D, virtual and augmented
reality (VR and AR), high frame rate (HFR), audio-visual, etc. This survey
provides an up-to-date and comprehensive review of these video quality
assessment studies. Specifically, we first review the subjective video quality
assessment methodologies and databases, which are necessary for validating the
performance of video quality metrics. Second, the objective video quality
assessment algorithms for general purposes are surveyed and concluded according
to the methodologies utilized in the quality measures. Third, we overview the
objective video quality assessment measures for specific applications and
emerging topics. Finally, the performances of the state-of-the-art video
quality assessment measures are compared and analyzed. This survey provides a
systematic overview of both classical works and recent progresses in the realm
of video quality assessment, which can help other researchers quickly access
the field and conduct relevant research.
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) - CLIPVQA:Video Quality Assessment via CLIP [56.94085651315878]
We propose an efficient CLIP-based Transformer method for the VQA problem ( CLIPVQA)
The proposed CLIPVQA achieves new state-of-the-art VQA performance and up to 37% better generalizability than existing benchmark VQA methods.
arXiv Detail & Related papers (2024-07-06T02:32:28Z) - RMT-BVQA: Recurrent Memory Transformer-based Blind Video Quality Assessment for Enhanced Video Content [7.778670210708263]
We propose a novel blind deep video quality assessment (VQA) method specifically for enhanced video content.
It employs a new Recurrent Memory Transformer (RMT) based network architecture to obtain video quality representations.
The extracted quality representations are then combined through linear regression to generate video-level quality indices.
arXiv Detail & Related papers (2024-05-14T14:01:15Z) - KVQ: Kwai Video Quality Assessment for Short-form Videos [24.5291786508361]
We establish the first large-scale Kaleidoscope short Video database for Quality assessment, KVQ, which comprises 600 user-uploaded short videos and 3600 processed videos.
We propose the first short-form video quality evaluator, i.e., KSVQE, which enables the quality evaluator to identify the quality-determined semantics with the content understanding of large vision language models.
arXiv Detail & Related papers (2024-02-11T14:37:54Z) - Towards A Better Metric for Text-to-Video Generation [102.16250512265995]
Generative models have demonstrated remarkable capability in synthesizing high-quality text, images, and videos.
We introduce a novel evaluation pipeline, the Text-to-Video Score (T2VScore)
This metric integrates two pivotal criteria: (1) Text-Video Alignment, which scrutinizes the fidelity of the video in representing the given text description, and (2) Video Quality, which evaluates the video's overall production caliber with a mixture of experts.
arXiv Detail & Related papers (2024-01-15T15:42:39Z) - Towards Explainable In-the-Wild Video Quality Assessment: A Database and
a Language-Prompted Approach [52.07084862209754]
We collect over two million opinions on 4,543 in-the-wild videos on 13 dimensions of quality-related factors.
Specifically, we ask the subjects to label among a positive, a negative, and a neutral choice for each dimension.
These explanation-level opinions allow us to measure the relationships between specific quality factors and abstract subjective quality ratings.
arXiv Detail & Related papers (2023-05-22T05:20:23Z) - 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) - Deep Quality Assessment of Compressed Videos: A Subjective and Objective
Study [23.3509109592315]
In the video coding process, the perceived quality of a compressed video is evaluated by full-reference quality evaluation metrics.
To solve this problem, it is critical to design no-reference compressed video quality assessment algorithms.
In this work, a semi-automatic labeling method is adopted to build a large-scale compressed video quality database.
arXiv Detail & Related papers (2022-05-07T10:50:06Z) - Study on the Assessment of the Quality of Experience of Streaming Video [117.44028458220427]
In this paper, the influence of various objective factors on the subjective estimation of the QoE of streaming video is studied.
The paper presents standard and handcrafted features, shows their correlation and p-Value of significance.
We take SQoE-III database, so far the largest and most realistic of its kind.
arXiv Detail & Related papers (2020-12-08T18:46:09Z)
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.