Video quality assessment (VQA) is an important element of various applications ranging from automatic video streaming to display technology. Furthermore, visual quality measurements require a balanced investigation of visual content and features. Previous studies have shown that the features extracted from a pretrained convolutional neural network are highly effective for a wide range of applications in image processing and computer vision. In this study, we developed a novel architecture for no-reference VQA based on the features obtained from pretrained convolutional neural networks, transfer learning, temporal pooling, and regression. In particular, we obtained solutions by only applying temporally pooled deep features and without using manually derived features. The proposed architecture was trained based on the recently published Konstanz natural video quality database (KoNViD-1k), which contains 1200 video sequences with authentic distortion unlike other publicly available databases. The experimental results obtained based on KoNViD-1k demonstrated that the proposed method performed better than other state-of-the-art algorithms. Furthermore, these results were confirmed by tests using the LIVE VQA database, which contains artificially distorted videos. Keywords No-reference video quality assessment · Convolutional neural network 1 IntroductionMultimedia technology and digital visual signal processing have developed rapidly during recent decades. Digital images and videos are very easy to create, transmit, store, and share. Owing to these developments, the design of reliable video quality assessment (VQA) algorithms has attracted considerable attention. Consequently, VQA has been the focus of many research studies and patents. Furthermore, the vast volume of user-created digital video content has led to the development of numerous VQA applications, which require reliable and effective quality monitoring [39]. B Domonkos Varga

A general-purpose no-reference video quality assessment algorithm based on a long short-term memory (LSTM) network and a pretrained convolutional neural network (CNN) is introduced. Considering video sequences as a time series of deep features extracted with the help of a CNN, an LSTM network is trained to predict subjective quality scores. In contrast to previous methods, the resulting algorithm was trained on the recently published Konstanz Natural Video Quality Database (KoNViD-1k), which is the only publicly available database that contains sequences with authentic distortions. The results of experiments on KoNViD-1k demonstrate that the proposed method outperforms other state-of-the-art algorithms. Furthermore, these results are also confirmed using tests on the LIVE Video Quality Assessment Database, which consists of artificially distorted videos.Keywords No-reference video quality assessment · Long short-term memory · Convolutional neural network B Domonkos Varga

Image quality assessment (IQA) is an important element of a broad spectrum of applications ranging from automatic video streaming to display technology. Furthermore, the measurement of image quality requires a balanced investigation of image content and features. Our proposed approach extracts visual features by attaching global average pooling (GAP) layers to multiple Inception modules of on an ImageNet database pretrained convolutional neural network (CNN). In contrast to previous methods, we do not take patches from the input image. Instead, the input image is treated as a whole and is run through a pretrained CNN body to extract resolution-independent, multi-level deep features. As a consequence, our method can be easily generalized to any input image size and pretrained CNNs. Thus, we present a detailed parameter study with respect to the CNN base architectures and the effectiveness of different deep features. We demonstrate that our best proposal—called MultiGAP-NRIQA—is able to outperform the state-of-the-art on three benchmark IQA databases. Furthermore, these results were also confirmed in a cross database test using the LIVE In the Wild Image Quality Challenge database.

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The goal of no-reference image quality assessment (NR-IQA) is to predict the quality of an image as perceived by human observers without using any pristine, reference images. In this study, an NR-IQA algorithm is proposed which is driven by a novel feature vector containing statistical and perceptual features. Different from other methods, normalized local fractal dimension distribution and normalized first digit distributions in the wavelet and spatial domains are incorporated into the statistical features. Moreover, powerful perceptual features, such as colorfulness, dark channel feature, entropy, and mean of phase congruency image, are also incorporated to the proposed model. Experimental results on five large publicly available databases (KADID-10k, ESPL-LIVE HDR, CSIQ, TID2013, and TID2008) show that the proposed method is able to outperform other state-of-the-art methods.

Abstract. This paper deals with the colorization of grayscale images. Recent papers have shown remarkable results on image colorization utilizing various deep architectures. Unlike previous methods, we perform colorization using a deep architecture and a reference image. Our architecture utilizes two parallel Convolutional Neural Networks which have the same structure. One CNN, which uses the reference image, helps the other CNN in color prediction for the input image. On the other hand, the second CNN, which uses the input image, helps to identify the areas which holds essential information about the color scheme of the scene. Comprehensive experiments and qualitative and quantitative evaluations were conducted on the images of SUN database and on other images. Quantitative evaluations are based on Peak Signal-to-Noise Ratio (PSNR) and on Quaternion Structural Similarity (QSSIM).

With the tremendous growth and usage of digital images, no-reference image quality assessment is becoming increasingly important. This paper presents in-depth analysis of Benford’s law inspired first digit distribution feature vectors for no-reference quality assessment of natural, screen-content, and synthetic images in various viewpoints. Benford’s law makes a prediction for the probability distribution of first digits in natural datasets. It has been applied among others for detecting fraudulent income tax returns, detecting scientific fraud, election forensics, and image forensics. In particular, our analysis is based on first digit distributions in multiple domains (wavelet coefficients, DCT coefficients, singular values, etc.) as feature vectors and the extracted features are mapped onto image quality scores. Extensive experiments have been carried out on seven large image quality benchmark databases. It has been demonstrated that first digit distributions are quality-aware features, and it is possible to reach or outperform the state-of-the-art with them.