基于多尺度DoG和GRNN的盲dibr合成图像质量评价

2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI:10.1109/NEUREL.2018.8587020

Dragana D. Sandić-Stanković, Dejan Bokan, Dragan D. Kukolj

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引用次数: 0

摘要

本文探讨了由高斯差分算子生成的多分辨率、多尺度带通图像表示在盲图像质量评估模型中的适用性。该模型基于广义回归神经网络(GRNN)。在dibr合成图像数据集上对该模型进行了评价，结果与人类感知结果一致。

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Blind DIBR-synthesized Image Quality Assessment using multi-scale DoG and GRNN

In this paper, we explore the suitability of multi-resolution and multi-scale band-pass image representation generated by difference of Gaussian (DoG) operator for blind image quality assessment model. The developed model is based on general regression neural network (GRNN). The proposed model is consistent with human perception when evaluated on DIBR-synthesized image dataset.

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来源期刊

2018 14th Symposium on Neural Networks and Applications (NEUREL)

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