Related papers: FAVER: Blind Quality Prediction of Variable Frame …
Video matting aims to predict the alpha mattes for each frame from a given input video sequence. Recent solutions to video matting have been dominated by deep convolutional neural networks (CNN) for the past few years, which have become the…
Existing video inference (VI) enhancement methods typically aim to improve performance by scaling up model sizes and employing sophisticated network architectures. While these approaches demonstrated state-of-the-art performance, they often…
The rise in video streaming applications has increased the demand for video quality assessment (VQA). In 2016, Netflix introduced Video Multi-Method Assessment Fusion (VMAF), a full reference VQA metric that strongly correlates with…
In the mobile communication field, some of the video applications boosted the interest of robust methods for video quality assessment. Out of all existing methods, We Preferred, No Reference Video Quality Assessment is the one which is most…
Many different parametric models for video quality assessment have been proposed in the past few years. This paper presents a review of nine recent models which cover a wide range of methodologies and have been validated for estimating…
In adaptive bitrate streaming, resolution cross-over refers to the point on the convex hull where the encoding resolution should switch to achieve better quality. Accurate cross-over prediction is crucial for streaming providers to optimize…
Visual autoregressive models achieve remarkable generation quality through next-scale predictions across multi-scale token pyramids. However, the conventional method uses uniform scale downsampling to build these pyramids, leading to…
In the age of streaming and surveillance compressed video enhancement has become a problem in need of constant improvement. Here, we investigate a way of improving the Multi-Frame Quality Enhancement approach. This approach consists of…
Neural fields, also known as coordinate-based or implicit neural representations, have shown a remarkable capability of representing, generating, and manipulating various forms of signals. For video representations, however, mapping…
Recent advances in quality adaptation algorithms leave adaptive bitrate (ABR) streaming architectures at a crossroads: When determining the sustainable video quality one may either rely on the information gathered at the client vantage…
The prevalence of user-generated content (UGC) on platforms such as YouTube and TikTok has rendered no-reference (NR) perceptual video quality assessment (VQA) vital for optimizing video delivery. Nonetheless, the characteristics of…
Video frame transmission delay is critical in real-time applications such as online video gaming, live show, etc. The receiving deadline of a new frame must catch up with the frame rendering time. Otherwise, the system will buffer a while,…
Deformable image registration establishes non-linear spatial correspondences between fixed and moving images. Deep learning-based deformable registration methods have been widely studied in recent years due to their speed advantage over…
Metrics for Visual Grounding (VG) in Visual Question Answering (VQA) systems primarily aim to measure a system's reliance on relevant parts of the image when inferring an answer to the given question. Lack of VG has been a common problem…
In recent years, video streaming applications have proliferated the demand for Video Quality Assessment VQA). Reduced reference video quality assessment (RR-VQA) is a category of VQA where certain features (e.g., texture, edges) of the…
A key factor in designing 3D systems is to understand how different visual cues and distortions affect the perceptual quality of 3D video. The ultimate way to assess video quality is through subjective tests. However, subjective evaluation…
In this paper, we investigate wireless streaming of multi-quality tiled 360 virtual reality (VR) videos from a multi-antenna server to multiple single-antenna users in a multi-carrier system. To capture the impact of field-of-view (FoV)…
Video Question Answering (VideoQA) aims to answer natural language questions according to the given videos. It has earned increasing attention with recent research trends in joint vision and language understanding. Yet, compared with…
We propose KFS-Bench, the first benchmark for key frame sampling in long video question answering (QA), featuring multi-scene annotations to enable direct and robust evaluation of sampling strategies. Key frame sampling is crucial for…
Traditional video quality assessment (VQA) methods evaluate localized picture quality and video score is predicted by temporally aggregating frame scores. However, video quality exhibits different characteristics from static image quality…