FAVER: Blind Quality Prediction of Variable Frame Rate Videos
Video quality assessment (VQA) remains an important and challenging problem that affects many applications at the widest scales. Recent advances in mobile devices and cloud computing techniques have made it possible to capture, process, and share high resolution, high frame rate (HFR) videos across the Internet nearly instantaneously. Being able to monitor and control the quality of these streamed videos can enable the delivery of more enjoyable content and perceptually optimized rate control. Accordingly, there is a pressing need to develop VQA models that can be deployed at enormous scales. While some recent effects have been applied to full-reference (FR) analysis of variable frame rate and HFR video quality, the development of no-reference (NR) VQA algorithms targeting frame rate variations has been little studied. Here, we propose a first-of-a-kind blind VQA model for evaluating HFR videos, which we dub the Framerate-Aware Video Evaluator w/o Reference (FAVER). FAVER uses extended models of spatial natural scene statistics that encompass space-time wavelet-decomposed video signals, to conduct efficient frame rate sensitive quality prediction. Our extensive experiments on several HFR video quality datasets show that FAVER outperforms other blind VQA algorithms at a reasonable computational cost. To facilitate reproducible research and public evaluation, an implementation of FAVER is being made freely available online: \url{https://github.com/uniqzheng/HFR-BVQA}.
Code (1)
Tasks
Cloud ComputingVideo Quality AssessmentVisual Question Answering (VQA)Similar Papers 제목 키워드 기반
Faver: Boosting LLM-based RTL Generation with Function Abstracted Verifiable Middleware
LLM-based RTL generation is an interesting research direction, as it holds the potential to liberate the least automated stage in the current chip design. However, due to the substantial semantic gap between high-level s…
Verification of Bit-Flip Attacks against Quantized Neural Networks
In the rapidly evolving landscape of neural network security, the resilience of neural networks against bit-flip attacks (i.e., an attacker maliciously flips an extremely small amount of bits within its parameter storage…
Neural Network SecurityQuantizationScale Guided Hypernetwork for Blind Super-Resolution Image Quality Assessment
With the emergence of image super-resolution (SR) algorithm, how to blindly evaluate the quality of super-resolution images has become an urgent task. However, existing blind SR image quality assessment (IQA) metrics mer…
Blind Super-ResolutionImage Quality AssessmentImage Super-ResolutionSuper-ResolutionLearned Scanpaths Aid Blind Panoramic Video Quality Assessment
Panoramic videos have the advantage of providing an immersive and interactive viewing experience. Nevertheless, their spherical nature gives rise to various and uncertain user viewing behaviors, which poses significant c…
Video Quality AssessmentA Comparative Evaluation of Temporal Pooling Methods for Blind Video Quality Assessment
Many objective video quality assessment (VQA) algorithms include a key step of temporal pooling of frame-level quality scores. However, less attention has been paid to studying the relative efficiencies of different pool…
Video Quality AssessmentVisual Question Answering (VQA)