Spatial Moment Pooling Improves Neural Image Assessment
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Abstract (arXiv)
In recent years, there has been widespread attention drawn to convolutional neural network (CNN) based blind image quality assessment (IQA). A large number of works start by extracting deep features from CNN. Then, those features are processed through spatial average pooling (SAP) and fully connected layers to predict quality. Inspired by full reference IQA and texture features, in this paper, we extend SAP (1st moment) into spatial moment pooling (SMP) by incorporating higher order moments (such as variance, skewness). Moreover, we provide learning friendly normalization to circumvent numerical issue when computing gradients of higher moments. Experimental results suggest that simply upgrading SAP to SMP significantly enhances CNN-based blind IQA methods and achieves state of the art performance.
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References · 10
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
- 2021MUSIQ: Multi-scale Image Quality Transformercited by 1,081
- 2017NIMA: Neural Image Assessmentcited by 389
- 2019KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessmentcited by 355
- 2020High-Fidelity Generative Image Compressioncited by 338
- 2019Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Networkcited by 302
- 2019From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Qualitycited by 223
- 2016EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesiscited by 178
- 2018Wide Activation for Efficient and Accurate Image Super-Resolutioncited by 65
- 2019Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalizationcited by 42
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