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Disregarding the Big Picture: Towards Local Image Quality Assessment | IEEE Conference Publication | IEEE Xplore

Disregarding the Big Picture: Towards Local Image Quality Assessment


Abstract:

Image quality has been studied almost exclusively as a global image property. It is common practice for IQA databases and metrics to quantify this abstract concept with a...Show More

Abstract:

Image quality has been studied almost exclusively as a global image property. It is common practice for IQA databases and metrics to quantify this abstract concept with a single number per image. We propose an approach to blind IQA based on a convolutional neural network (patchnet) that was trained on a novel set of 32,000 individually annotated patches of 64×64 pixel. We use this model to generate spatially small local quality maps of images taken from KonIQ-10k, a large and diverse in-the-wild database of authentically distorted images. We show that our local quality indicator correlates well with global MOS, going beyond the predictive ability of quality related attributes such as sharpness. Averaging of patchnet predictions already outperforms classical approaches to global MOS prediction that were trained to include global image features. We additionally experiment with a generic second-stage aggregation CNN to estimate mean opinion scores. Our latter model performs comparable to the state of the art with a PLCC of 0.81 on KonIQ-10k.
Date of Conference: 29 May 2018 - 01 June 2018
Date Added to IEEE Xplore: 13 September 2018
ISBN Information:
Electronic ISSN: 2472-7814
Conference Location: Cagliari, Italy

References

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