No-reference image quality assessment using Prewitt magnitude based on convolutional neural networks

Abstract

No-reference image quality assessment is of great importance to numerous image processing applications, and various methods have been widely studied with promising results. These methods exploit handcrafted features in the transformation or space domain that are discriminated for image degradations. However, abundant a priori knowledge is required to extract these handcrafted features. The convolutional neural network (CNN) is recently introduced into the no-reference image quality assessment, which integrates feature learning and regression into one optimization process. Therefore, the network structure generates an effective model for estimating image quality. However, the image quality score obtained by the CNN is based on the mean of all of the image patch scores without considering the human visual system, such as edges and contour of images. In this paper, we combine the CNN and the Prewitt magnitude of segmented images and obtain the image quality score using the mean of all the products of the image patch scores and weights based on the result of segmented images. Experimental results on various image distortion types demonstrate that the proposed algorithm achieves good performance.Keywords No-reference image quality assessment · Convolutional neural networks (CNNs) · Graph-based image segmentation · Prewitt magnitude

Image Quality Assessment Based on Joint Quality-Aware Representation Construction in Multiple Domains

“…More recently, researchers tend to construct more complex weighting function or employ machine learning techniques to develop the fusion scheme [4]. -nearest neighbor (KNN), convolutional neural network (CNN), and SVR are the most commonly used machine learning tools [26].…”

Saliency-based deep convolutional neural network for no-reference image quality assessment

“…But the depth of their CNN model may limit the power of feature extraction and assigning eqaul weight to all patches may not consistent with HVS. One close work to ours was proposed by Li et al [13], which also combines CNNs with a saliency algorithm of gradients. In their work, a two-layer CNN model was used for feature extraction.…”

“…For the training-based method, it relies on a set of features learned from images and then a classifier is trained. The training-based method can be considered as a traditional machine learning task [10,11,13,25,27]. Therefore how to extract discriminant features is a common question between vision recognition tasks and training-based IQA.…”

“…It has been shown that CNN-learned features outperform hand-designed ones (local binary patterns or scale-invariant feature transform) in many areas, such as object classification [7], face gender recognition [8] or fashion detection [9]. More recently, CNNs have been introduced to NR-IQA and achieved state-of-the-art results [10,11,13]. It has also been shown that the depth of CNNs plays an important role in feature extraction [7,8,22,23].…”

“…To introduce HVS, we combine CNNs with the saliency map algorithm of [19], referred as Saliency-based DCNN (SDCNN). One close work to ours is that of [13] in which a gradient map is used to measure the importance of each patch, see Section 2.…”