A Localized No-Reference Blurriness Measure for Omnidirectional Images and Video. Fassold, H. & Wechtitsch, S. In 2019 27th European Signal Processing Conference (EUSIPCO), pages 1-5, Sep., 2019. Paper doi abstract bibtex Blurriness is a defect commonly occurring in conventional video but also in omnidirectional video. In this work, we propose a novel no-reference blurriness measure for images captured with omnidirectional video cameras. These images present unique challenges for quality measures due to their size and due to the equirectangular projection which is commonly employed for them. We base upon a state of the art algorithm and adapt it for the specifics of omnidirectional images. Furthermore, we extend it with a coarse-scale blurriness map for measuring spatially varying blur. We present a novel ground truth dataset which was generated by adding spatially varying gaussian blur of different magnitude in a viewport-centric way. Experiments with the proposed algorithm on this dataset show a strong correlation of the localized blurriness measure with the ground truth.
@InProceedings{8903173,
author = {H. Fassold and S. Wechtitsch},
booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
title = {A Localized No-Reference Blurriness Measure for Omnidirectional Images and Video},
year = {2019},
pages = {1-5},
abstract = {Blurriness is a defect commonly occurring in conventional video but also in omnidirectional video. In this work, we propose a novel no-reference blurriness measure for images captured with omnidirectional video cameras. These images present unique challenges for quality measures due to their size and due to the equirectangular projection which is commonly employed for them. We base upon a state of the art algorithm and adapt it for the specifics of omnidirectional images. Furthermore, we extend it with a coarse-scale blurriness map for measuring spatially varying blur. We present a novel ground truth dataset which was generated by adding spatially varying gaussian blur of different magnitude in a viewport-centric way. Experiments with the proposed algorithm on this dataset show a strong correlation of the localized blurriness measure with the ground truth.},
keywords = {feature extraction;image enhancement;image resolution;image restoration;solid modelling;video cameras;video signal processing;omnidirectional video cameras;quality measures;equirectangular projection;omnidirectional images;coarse-scale blurriness map;localized blurriness measure;localized no-reference blurriness measure;omnidirectional video;Image edge detection;Signal processing algorithms;Cameras;Distortion;Visualization;Distortion measurement;image quality measure;no-reference blur assessment;omnidirectional image;360°;video;VR},
doi = {10.23919/EUSIPCO.2019.8903173},
issn = {2076-1465},
month = {Sep.},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570528039.pdf},
}
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