VMAF Re-implementation on PyTorch: Some Experimental Results

Fuente: arXiv
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Autori principali: Aistov, Kirill, Koroteev, Maxim
Natura: Preprint
Pubblicazione: 2023
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author Aistov, Kirill
Koroteev, Maxim
author_facet Aistov, Kirill
Koroteev, Maxim
contents Based on the standard VMAF implementation we propose an implementation of VMAF using PyTorch framework. For this implementation comparisons with the standard (libvmaf) show the discrepancy $\lesssim 10^{-2}$ in VMAF units. We investigate gradients computation when using VMAF as an objective function and demonstrate that training using this function does not result in ill-behaving gradients. The implementation is then used to train a preprocessing filter. It is demonstrated that its performance is superior to the unsharp masking filter. The resulting filter is also easy for implementation and can be applied in video processing tasks for video copression improvement. This is confirmed by the results of numerical experiments.
format Preprint
id arxiv_https___arxiv_org_abs_2310_15578
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle VMAF Re-implementation on PyTorch: Some Experimental Results
Aistov, Kirill
Koroteev, Maxim
Machine Learning
Computer Vision and Pattern Recognition
Based on the standard VMAF implementation we propose an implementation of VMAF using PyTorch framework. For this implementation comparisons with the standard (libvmaf) show the discrepancy $\lesssim 10^{-2}$ in VMAF units. We investigate gradients computation when using VMAF as an objective function and demonstrate that training using this function does not result in ill-behaving gradients. The implementation is then used to train a preprocessing filter. It is demonstrated that its performance is superior to the unsharp masking filter. The resulting filter is also easy for implementation and can be applied in video processing tasks for video copression improvement. This is confirmed by the results of numerical experiments.
title VMAF Re-implementation on PyTorch: Some Experimental Results
topic Machine Learning
Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2310.15578