We propose a no-reference image quality assessment (NR-IQA) approach that learns from rankings (RankIQA). To address the problem of limited IQA dataset size, we train a Siamese Network to rank images in terms of image quality by using synthetically generated distortions for which relative image quality is known. These ranked image sets can be automatically generated without laborious human labeling. We then use fine-tuning to transfer the knowledge represented in the trained Siamese Network to a traditional CNN that estimates absolute image quality from single images. We demonstrate how our approach can be made significantly more efficient than traditional Siamese Networks by forward propagating a batch of images through a single network and backpropagating gradients derived from all pairs of images in the batch. Experiments on the TID2013 benchmark show that we improve the state-of-the-art by over 5 . Furthermore, on the LIVE benchmark we show that our approach is superior to existing NR-IQA techniques and that we even outperform the state-of-the-art in full-reference IQA (FR-IQA) methods without having to resort to high-quality reference images to infer IQA.
Deep learning for NR-IQA. In recent years several works have used deep learning for NR-IQA @cite_8 @cite_31 @cite_30 . One of the main drawbacks of deep networks is the need for large labeled datasets, which are currently not available for NR-IQA research. To address this problem @cite_31 consider small @math patches rather than images, thereby greatly augmenting the number of training examples. The authors of @cite_20 @cite_30 follow the same pipeline. @cite_30 the authors design a multi-task CNN to learn the type of distortions and image quality simultaneously. Bianco at al. @cite_8 propose to use a pre-trained network to mitigate the lack of training data. They extract features from a pre-trained model fine-tuned on an IQA dataset. These features are then used to train an SVR model to map features to IQA scores.
@cite_30 @cite_31 @cite_20 @cite_8
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This paper presents a no reference image (NR) quality assessment (IQA) method based on a deep convolutional neural network (CNN). The CNN takes unpreprocessed image patches as an input and estimates the quality without employing any domain knowledge. By that, features and natural scene statistics are learnt purely data driven and combined with pooling and regression in one framework. We evaluate the network on the LIVE database and achieve a linear Pearson correlation superior to state-of-the-art NR IQA methods. We also apply the network to the image forensics task of decoder-sided quantization parameter estimation and also here achieve correlations of r = 0.989. Nowadays, Deep Neural Networks have been applied to many applications (such as classification, denoising and inpainting) and achieved impressive performance. However, most of these works pay much attention to describe how to construct the relative framework but ignore to provide a clear and intuitive understanding of why their framework performs so well. In this paper, we present a general-purpose no-reference (NR) image quality assessment (IQA) framework based on deep neural network and give insight into the operation of this network. In this NR-IQA framework, simple features are extracted by a new multiscale directional transform (shearlet transform) and the sum of subband coefficient amplitudes (SSCA) is utilized as primary features to describe the behavior of natural images and distorted images. Then, stacked autoencoders are applied as 'evolution process' to 'amplify' the primary features and make them more discriminative. Finally, by translating the NR-IQA problem into classification problem, the differences of evolved features are identified by softmax classifier. Moreover, we have also incorporated some visualization techniques to analysis and visualize this NR-IQA framework. The resulting algorithm, which we name SESANIA (ShEarlet and Stacked Autoencoders based No-reference Image quality Assessment) is tested on several database (LIVE, Multiply Distorted LIVE and TID2008) individually and combined together. Experimental results demonstrate the excellent performance of SESANIA, and we also give intuitive explanations of how it works and why it works well. In addition, SESANIA is extended to estimate quality in local regions. Further experiments demonstrate the local quality estimation ability of SESANIA on images with local distortions. A NR-IQA framework is proposed using shearlet transform and deep neural networks.Intuitive explanations of how this framework works and why it works well are given.This framework is extended to estimate image quality in local regions. Recently, neuron activations extracted from a pre-trained convolutional neural network (CNN) show promising performance in various visual tasks. However, due to the domain and task bias, using the features generated from the model pre-trained for image classification as image representations for instance retrieval is problematic. In this paper, we propose quartet-net learning to improve the discriminative power of CNN features for instance retrieval. The general idea is to map the features into a space where the image similarity can be better evaluated. Our network differs from the traditional Siamese-net in two ways. First, we adopt a double-margin contrastive loss with a dynamic margin tuning strategy to train the network which leads to more robust performance. Second, we introduce in the mimic learning regularization to improve the generalization ability of the network by preventing it from overfitting to the training data. Catering for the network learning, we collect a large-scale dataset, namely GeoPair, which consists of 68k matching image pairs and 63k non-matching pairs. Experiments on several standard instance retrieval datasets demonstrate the effectiveness of our method. This paper addresses the problem of general-purpose No-Reference Image Quality Assessment (NR-IQA) with the goal of developing a real-time, cross-domain model that can predict the quality of distorted images without prior knowledge of non-distorted reference images and types of distortions present in these images. The contributions of our work are two-fold: first, the proposed method is highly efficient. NR-IQA measures are often used in real-time imaging or communication systems, therefore it is important to have a fast NR-IQA algorithm that can be used in these real-time applications. Second, the proposed method has the potential to be used in multiple image domains. Previous work on NR-IQA focus primarily on predicting quality of natural scene image with respect to human perception, yet, in other image domains, the final receiver of a digital image may not be a human. The proposed method consists of the following components: (1) a local feature extractor, (2) a global feature extractor and (3) a regression model. While previous approaches usually treat local feature extraction and regression model training independently, we propose a supervised method based on back-projection, which links the two steps by learning a compact set of filters which can be applied to local image patches to obtain discriminative local features. Using a small set of filters, the proposed method is extremely fast. We have tested this method on various natural scene and document image datasets and obtained state-of-the-art results.