6 research outputs found
Blind Quality Assessment for in-the-Wild Images via Hierarchical Feature Fusion and Iterative Mixed Database Training
Image quality assessment (IQA) is very important for both end-users and
service-providers since a high-quality image can significantly improve the
user's quality of experience (QoE) and also benefit lots of computer vision
algorithms. Most existing blind image quality assessment (BIQA) models were
developed for synthetically distorted images, however, they perform poorly on
in-the-wild images, which are widely existed in various practical applications.
In this paper, we propose a novel BIQA model for in-the-wild images by
addressing two critical problems in this field: how to learn better
quality-aware feature representation, and how to solve the problem of
insufficient training samples in terms of their content and distortion
diversity. Considering that perceptual visual quality is affected by both
low-level visual features (e.g. distortions) and high-level semantic
information (e.g. content), we first propose a staircase structure to
hierarchically integrate the features from intermediate layers into the final
feature representation, which enables the model to make full use of visual
information from low-level to high-level. Then an iterative mixed database
training (IMDT) strategy is proposed to train the BIQA model on multiple
databases simultaneously, so the model can benefit from the increase in both
training samples and image content and distortion diversity and can learn a
more general feature representation. Experimental results show that the
proposed model outperforms other state-of-the-art BIQA models on six
in-the-wild IQA databases by a large margin. Moreover, the proposed model shows
an excellent performance in the cross-database evaluation experiments, which
further demonstrates that the learned feature representation is robust to
images with diverse distortions and content. The code will be released publicly
for reproducible research
Multi-modal convolutional parameterisation network for guided image inverse problems
There are several image inverse tasks, such as inpainting or super-resolution, which can be solved using deep internal learning, a paradigm that involves employing deep neural networks to find a solution by learning from the sample itself rather than a dataset. For example, Deep Image Prior is a technique based on fitting a convolutional neural network to output the known parts of the image (such as non-inpainted regions or a low-resolution version of the image). However, this approach is not well adjusted for samples composed of multiple modalities. In some domains, such as satellite image processing, accommodating multi-modal representations could be beneficial or even essential. In this work, Multi-Modal Convolutional Parameterisation Network (MCPN) is proposed, where a convolutional neural network approximates shared information between multiple modes by combining a core shared network with modality-specific head networks. The results demonstrate that these approaches can significantly outperform the single-mode adoption of a convolutional parameterisation network on guided image inverse problems of inpainting and super-resolution