. Current deep video quality assessment (VQA) methods are usually with high computational costs when evaluating high-resolution videos. This cost hinders them from learning better video-quality-related representations via end-to-end training. Existing approaches usually con-sider naive sampling to reduce the computational cost, such as resizing and cropping . However, they obviously corrupt quality-related information in videos and are thus not optimal to learn good representations for VQA. Therefore, there is an eager need to design a new quality-retained sampling scheme for VQA. In this paper, we propose Grid Mini-patch Sampling (GMS), which allows consideration of local quality by sampling patches at their raw resolution and covers global quality with contextual relations via mini-patches sampled in uniform grids. These mini-patches are spliced and aligned temporally, named as fragments . We further build the Fragment Attention Network (FANet) specially designed to ac-commodate fragments as inputs. Consisting of fragments and FANet, the proposed FrAgment Sample Transformer for VQA ( FAST-VQA ) enables efficient end-to-end deep VQA and learns effective video-quality-related representations. It improves state-of-the-art accuracy by around 10% while reducing 99 . 5% FLOPs on 1080P high-resolution videos. The newly learned video-quality-related representations can also be transferred into smaller VQA datasets and boost the performance on these scenarios. Extensive experiments show that FAST-VQA has good performance on inputs of various resolutions while retaining high efficiency. We publish our code at https://github.com/timothyhtimothy/FAST-VQA .

FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling

—The increased resolution of real-world videos presents a dilemma between efﬁciency and accuracy for deep Video Quality Assessment (VQA). On the one hand, keeping the original resolution will lead to unacceptable computational costs. On the other hand, existing practices, such as resizing and cropping, will change the quality of original videos due to the loss of details and contents, and are therefore harmful to quality assessment. With the obtained insight from the study of spatial-temporal redundancy in the human visual system and visual coding theory, we observe that quality information around a neighbourhood is typically similar, motivating us to investigate an effective quality-sensitive neighbourhood representatives scheme for VQA. In this work, we propose a uniﬁed scheme, spatial-temporal grid mini-cube sampling (St-GMS) to get a novel type of sample, named fragments . Full-resolution videos are ﬁrst divided into mini-cubes with preset spatial-temporal grids, then the temporal-aligned quality representatives are sampled to compose the fragments that serve as inputs for VQA. In addition, we design the Fragment Attention Network (FANet), a network architecture tailored speciﬁcally for fragments. With fragments and FANet, the proposed efﬁcient end-to-end FAST-VQA and FasterVQA achieve signiﬁcantly better performance than existing approaches on all VQA benchmarks while requiring only 1/1612 FLOPs compared to the current state-of-the-art. Codes, models and demos are available at https://github.com/timothyhtimothy/FAST-VQA-and-FasterVQA.

Neighbourhood Representative Sampling for Efficient End-to-end Video Quality Assessment

User-generated-content (UGC) videos have dominated the Internet during recent years. While it is well-recognized that the perceptual quality of these videos can be affected by diverse factors, few existing methods explicitly explore the effects of different factors in video quality assessment (VQA) for UGC videos, i.e. the UGC-VQA problem . In this work, we make the ﬁrst attempt to disentangle the effects of aesthetic quality issues and technical quality issues risen by the complicated video generation processes in the UGC-VQA problem. To overcome the absence of respective supervisions during disentanglement, we propose the Limited View Biased Supervisions (LVBS) scheme where two separate evaluators are trained with decomposed views speciﬁcally designed for each issue. Composed of an Aesthetic Quality Evaluator (AQE) and a Technical Quality Evaluator (TQE) under the LVBS scheme, the proposed Disentangled Objective Video Quality Evaluator ( DOVER ) reach excellent performance (0.91 SRCC for KoNViD-1k, 0.89 SRCC for LSVQ, 0.88 SRCC for YouTube-UGC) in the UGC-VQA problem. More importantly, our blind subjective studies prove that the separate evaluators in DOVER can effectively match human perception on respective disentangled quality issues. Codes and demos are released in https://github.com/teowu/dover .

Disentangling Aesthetic and Technical Effects for Video Quality Assessment of User Generated Content

The core aspect of this work is a review for player detection and tracing. Coaches and players prepare widely by studying the opponents attacking and self-protecting formations, plays and metrics before every game. Moving object detection is one of the serious problems in the field of surveillance, traffic monitoring, computer vision, player tracking in sports video and so forth. Detecting the objects in the video and tracking its movement to recognise its qualities have been a demanding examination zone in the area of computer image and image processing. The core aspect of this work is a review for player detection and tracking. As coaches and players prepare widely by studying the opponents offensive and defensive formations, plays and metrics before every game, tracking of player becomes a major problem because of different problems in appearance, occlusion and so on. This work offers an analysis on detection of objects, segmentation of objects and tracking of objects. It also provides the study of comparison of diverse methods used for player tracking.

Player Tracking in Sports Video Using Optical Flow Analysis

Recent learning-based video quality assessment (VQA) algorithms are expensive to implement due to the cost of data collection of human quality opinions, and are less robust across various scenarios due to the biases of these opinions. This motivates our exploration on opinion-unaware (a.k.a zero-shot) VQA approaches. Existing approaches only considers low-level naturalness in spatial or temporal domain, without considering impacts from high-level semantics. In this work, we introduce an explicit semantic affinity index for opinion-unaware VQA using text-prompts in the contrastive language-image pre-training (CLIP) model. We also aggregate it with different traditional low-level naturalness indexes through gaussian normalization and sigmoid rescaling strategies. Composed of aggregated semantic and technical metrics, the proposed Blind Unified Opinion-Unaware Video Quality Index via Semantic and Technical Metric Aggregation (BUONA-VISTA) outperforms existing opinion-unaware VQA methods by at least 20% improvements, and is more robust than opinion-aware approaches.

Exploring Opinion-unaware Video Quality Assessment with Semantic Affinity Criterion

With the rapid growth of in-the-wild videos taken by non-specialists, blind video quality assessment (VQA) has become a challenging and demanding problem. Although lots of efforts have been made to solve this problem, it remains unclear how the human visual system (HVS) relates to the temporal quality of videos. Meanwhile, recent work has found that the frames of natural video transformed into the perceptual domain of the HVS tend to form a straight trajectory of the representations. With the obtained insight that distortion impairs the perceived video quality and results in a curved trajectory of the perceptual representation, we propose a temporal perceptual quality index (TPQI) to measure the temporal distortion by describing the graphic morphology of the representation. Specifically, we first extract the video perceptual representations from the lateral geniculate nucleus (LGN) and primary visual area (V1) of the HVS, and then measure the straightness and compactness of their trajectories to quantify the degradation in naturalness and content continuity of video. Experiments show that the perceptual representation in the HVS is an effective way of predicting subjective temporal quality, and thus TPQI can, for the first time, achieve comparable performance to the spatial quality metric and be even more effective in assessing videos with large temporal variations. We further demonstrate that by combining with NIQE, a spatial quality metric, TPQI can achieve top performance over popular in-the-wild video datasets. More importantly, TPQI does not require any additional information beyond the video being evaluated and thus can be applied to any datasets without parameter tuning. Source code is available at https://github.com/UoLMM/TPQI-VQA.

/pdf/exploring-the-effectiveness-of-video-perceptual-1gwp67py.pdf

Exploring the Effectiveness of Video Perceptual Representation in Blind Video Quality Assessment

The rapid increase in user-generated-content (UGC) videos calls for the development of effective video quality assessment (VQA) algorithms. However, the objective of the UGC-VQA problem is still ambiguous and can be viewed from two perspectives: the technical perspective, measuring the perception of distortions; and the aesthetic perspective, which relates to preference and recommendation on contents. To understand how these two perspectives affect overall subjective opinions in UGC-VQA, we conduct a large-scale subjective study to collect human quality opinions on overall quality of videos as well as perceptions from aesthetic and technical perspectives. The collected Disentangled Video Quality Database (DIVIDE-3k) confirms that human quality opinions on UGC videos are universally and inevitably affected by both aesthetic and technical perspectives. In light of this, we propose the Disentangled Objective Video Quality Evaluator (DOVER) to learn the quality of UGC videos based on the two perspectives. The DOVER proves state-of-the-art performance in UGC-VQA under very high efficiency. With perspective opinions in DIVIDE-3k, we further propose DOVER++, the first approach to provide reliable clear-cut quality evaluations from a single aesthetic or technical perspective. Code at https://github.com/VQAssessment/DOVER.

Chao-Ju Chen

Papers

Exploring the Effectiveness of Video Perceptual Representation in Blind Video Quality Assessment

Neighbourhood Representative Sampling for Efficient End-to-end Video Quality Assessment

Disentangling Aesthetic and Technical Effects for Video Quality Assessment of User Generated Content

Exploring Opinion-unaware Video Quality Assessment with Semantic Affinity Criterion

Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives