147,896 research outputs found
BIQ2021: A Large-Scale Blind Image Quality Assessment Database
The assessment of the perceptual quality of digital images is becoming
increasingly important as a result of the widespread use of digital multimedia
devices. Smartphones and high-speed internet are just two examples of
technologies that have multiplied the amount of multimedia content available.
Thus, obtaining a representative dataset, which is required for objective
quality assessment training, is a significant challenge. The Blind Image
Quality Assessment Database, BIQ2021, is presented in this article. By
selecting images with naturally occurring distortions and reliable labeling,
the dataset addresses the challenge of obtaining representative images for
no-reference image quality assessment. The dataset consists of three sets of
images: those taken without the intention of using them for image quality
assessment, those taken with intentionally introduced natural distortions, and
those taken from an open-source image-sharing platform. It is attempted to
maintain a diverse collection of images from various devices, containing a
variety of different types of objects and varying degrees of foreground and
background information. To obtain reliable scores, these images are
subjectively scored in a laboratory environment using a single stimulus method.
The database contains information about subjective scoring, human subject
statistics, and the standard deviation of each image. The dataset's Mean
Opinion Scores (MOS) make it useful for assessing visual quality. Additionally,
the proposed database is used to evaluate existing blind image quality
assessment approaches, and the scores are analyzed using Pearson and Spearman's
correlation coefficients. The image database and MOS are freely available for
use and benchmarking
Blind image quality evaluation using perception based features
This paper proposes a novel no-reference Perception-based Image Quality Evaluator (PIQUE) for real-world imagery. A majority of the existing methods for blind image quality assessment rely on opinion-based supervised learning for quality score prediction. Unlike these methods, we propose an opinion unaware methodology that attempts to quantify distortion without the need for any training data. Our method relies on extracting local features for predicting quality. Additionally, to mimic human behavior, we estimate quality only from perceptually significant spatial regions. Further, the choice of our features enables us to generate a fine-grained block level distortion map. Our algorithm is competitive with the state-of-the-art based on evaluation over several popular datasets including LIVE IQA, TID & CSIQ. Finally, our algorithm has low computational complexity despite working at the block-level
Using the Natural Scenes’ Edges for Assessing Image Quality Blindly and Efficiently
Two real blind/no-reference (NR) image quality assessment (IQA) algorithms in the spatial domain are developed. To measure image quality, the introduced approach uses an unprecedented concept for gathering a set of novel features based on edges of natural scenes. The enhanced sensitivity of the human eye to the information carried by edge and contour of an image supports this claim. The effectiveness of the proposed technique in quantifying image quality has been studied. The gathered features are formed using both Weibull distribution statistics and two sharpness functions to devise two separate NR IQA algorithms. The presented algorithms do not need training on databases of human judgments or even prior knowledge about expected distortions, so they are real NR IQA algorithms. In contrast to the most general no-reference IQA, the model used for this study is generic and has been created in such a way that it is not specified to any particular distortion type. When testing the proposed algorithms on LIVE database, experiments show that they correlate well with subjective opinion scores. They also show that the introduced methods significantly outperform the popular full-reference peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) methods. Besides they outperform the recently developed NR natural image quality evaluator (NIQE) model
Blind Quality Assessment for Image Superresolution Using Deep Two-Stream Convolutional Networks
Numerous image superresolution (SR) algorithms have been proposed for
reconstructing high-resolution (HR) images from input images with lower spatial
resolutions. However, effectively evaluating the perceptual quality of SR
images remains a challenging research problem. In this paper, we propose a
no-reference/blind deep neural network-based SR image quality assessor
(DeepSRQ). To learn more discriminative feature representations of various
distorted SR images, the proposed DeepSRQ is a two-stream convolutional network
including two subcomponents for distorted structure and texture SR images.
Different from traditional image distortions, the artifacts of SR images cause
both image structure and texture quality degradation. Therefore, we choose the
two-stream scheme that captures different properties of SR inputs instead of
directly learning features from one image stream. Considering the human visual
system (HVS) characteristics, the structure stream focuses on extracting
features in structural degradations, while the texture stream focuses on the
change in textural distributions. In addition, to augment the training data and
ensure the category balance, we propose a stride-based adaptive cropping
approach for further improvement. Experimental results on three publicly
available SR image quality databases demonstrate the effectiveness and
generalization ability of our proposed DeepSRQ method compared with
state-of-the-art image quality assessment algorithms
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