26,096 research outputs found
No-reference image quality assessment through the von Mises distribution
An innovative way of calculating the von Mises distribution (VMD) of image
entropy is introduced in this paper. The VMD's concentration parameter and some
fitness parameter that will be later defined, have been analyzed in the
experimental part for determining their suitability as a image quality
assessment measure in some particular distortions such as Gaussian blur or
additive Gaussian noise. To achieve such measure, the local R\'{e}nyi entropy
is calculated in four equally spaced orientations and used to determine the
parameters of the von Mises distribution of the image entropy. Considering
contextual images, experimental results after applying this model show that the
best-in-focus noise-free images are associated with the highest values for the
von Mises distribution concentration parameter and the highest approximation of
image data to the von Mises distribution model. Our defined von Misses fitness
parameter experimentally appears also as a suitable no-reference image quality
assessment indicator for no-contextual images.Comment: 29 pages, 11 figure
No-Reference Quality Assessment for 360-degree Images by Analysis of Multi-frequency Information and Local-global Naturalness
360-degree/omnidirectional images (OIs) have achieved remarkable attentions
due to the increasing applications of virtual reality (VR). Compared to
conventional 2D images, OIs can provide more immersive experience to consumers,
benefitting from the higher resolution and plentiful field of views (FoVs).
Moreover, observing OIs is usually in the head mounted display (HMD) without
references. Therefore, an efficient blind quality assessment method, which is
specifically designed for 360-degree images, is urgently desired. In this
paper, motivated by the characteristics of the human visual system (HVS) and
the viewing process of VR visual contents, we propose a novel and effective
no-reference omnidirectional image quality assessment (NR OIQA) algorithm by
Multi-Frequency Information and Local-Global Naturalness (MFILGN).
Specifically, inspired by the frequency-dependent property of visual cortex, we
first decompose the projected equirectangular projection (ERP) maps into
wavelet subbands. Then, the entropy intensities of low and high frequency
subbands are exploited to measure the multi-frequency information of OIs.
Besides, except for considering the global naturalness of ERP maps, owing to
the browsed FoVs, we extract the natural scene statistics features from each
viewport image as the measure of local naturalness. With the proposed
multi-frequency information measurement and local-global naturalness
measurement, we utilize support vector regression as the final image quality
regressor to train the quality evaluation model from visual quality-related
features to human ratings. To our knowledge, the proposed model is the first
no-reference quality assessment method for 360-degreee images that combines
multi-frequency information and image naturalness. Experimental results on two
publicly available OIQA databases demonstrate that our proposed MFILGN
outperforms state-of-the-art approaches
No-Reference Light Field Image Quality Assessment Based on Micro-Lens Image
Light field image quality assessment (LF-IQA) plays a significant role due to
its guidance to Light Field (LF) contents acquisition, processing and
application. The LF can be represented as 4-D signal, and its quality depends
on both angular consistency and spatial quality. However, few existing LF-IQA
methods concentrate on effects caused by angular inconsistency. Especially,
no-reference methods lack effective utilization of 2-D angular information. In
this paper, we focus on measuring the 2-D angular consistency for LF-IQA. The
Micro-Lens Image (MLI) refers to the angular domain of the LF image, which can
simultaneously record the angular information in both horizontal and vertical
directions. Since the MLI contains 2-D angular information, we propose a
No-Reference Light Field image Quality assessment model based on MLI (LF-QMLI).
Specifically, we first utilize Global Entropy Distribution (GED) and Uniform
Local Binary Pattern descriptor (ULBP) to extract features from the MLI, and
then pool them together to measure angular consistency. In addition, the
information entropy of Sub-Aperture Image (SAI) is adopted to measure spatial
quality. Extensive experimental results show that LF-QMLI achieves the
state-of-the-art performance
NO REFERENCE IMAGE QUALITY ASSESSMENT
A no-reference image quality assessment (NR-IQA) technique can measure the visual distortion in an image without any reference image data. NR-IQA aims to predict the image quality based on the quality perceived by the Human Visual System (HVS). Image distortions can be caused through the acquisition, compression or transmission of digital images. From the several types of image distortions, JPEG and JPEG2000 compression distortions, addition of white noise, Gaussian blur, and fast fading are the most common. Several approaches were proposed to tackle this problem, some were distortion specific and some were general purpose. Of these, Convolutional Neural Networks (CNN) based approaches have proven to be efficient in predicting quality of the images. Most of these models are trained and tested only for single distortion general purpose images, but in the real world, the images contain more than one distortion type. This Work mainly focusses on using deep convolutional neural networks (DCNNs) for NR-IQA, identifying the different distortion types that are present in the image using distortion type classifiers and also, find the distortion quality of each distortion types using a network of DCNNs. We name this novel approach to be multiple DCNN (MDCNN). We fine tune the networks with different activation functions, optimizers and different tunable parameters in CNNs for the better accuracy. Also, we experiment on different patch sizes that can affect the performance of the system. This proposed model is trained on the LIVE II database and its performance is tested on the CSIQ, and TID 2008 databases which are single distortion. These models achieved high correlation coefficients and accuracy scores on these databases. We further provide the visualization of the inner layers of the DCNN for better understanding of the image quality
Image Quality Assessment by Saliency Maps
Image Quality Assessment (IQA) is an interesting challenge for image processing applications. The goal of IQA is to replace human judgement of perceived image quality with a machine evaluation. A large number of methods have been proposed to evaluate the quality of an image which may be corrupted by noise, distorted during acquisition, transmission, compression, etc. Many methods, in some cases, do not agree with human judgment because they are not correlated with human visual perception. In the last years the most modern IQA models and metrics considered visual saliency as a fundamental issue. The aim of visual saliency is to produce a saliency map that replicates the human visual system (HVS) behaviour in visual attention process. In this paper we show the relationship between different kind of visual saliency maps and IQA measures. We particularly perform a lot of comparisons between Saliency-Based IQA Measures and traditional Objective IQA Measure. In Saliency scientific literature there are many different approaches for saliency maps, we want to investigate which is best one for IQA metrics
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