3,430 research outputs found
Terahertz Security Image Quality Assessment by No-reference Model Observers
To provide the possibility of developing objective image quality assessment
(IQA) algorithms for THz security images, we constructed the THz security image
database (THSID) including a total of 181 THz security images with the
resolution of 127*380. The main distortion types in THz security images were
first analyzed for the design of subjective evaluation criteria to acquire the
mean opinion scores. Subsequently, the existing no-reference IQA algorithms,
which were 5 opinion-aware approaches viz., NFERM, GMLF, DIIVINE, BRISQUE and
BLIINDS2, and 8 opinion-unaware approaches viz., QAC, SISBLIM, NIQE, FISBLIM,
CPBD, S3 and Fish_bb, were executed for the evaluation of the THz security
image quality. The statistical results demonstrated the superiority of Fish_bb
over the other testing IQA approaches for assessing the THz image quality with
PLCC (SROCC) values of 0.8925 (-0.8706), and with RMSE value of 0.3993. The
linear regression analysis and Bland-Altman plot further verified that the
Fish__bb could substitute for the subjective IQA. Nonetheless, for the
classification of THz security images, we tended to use S3 as a criterion for
ranking THz security image grades because of the relatively low false positive
rate in classifying bad THz image quality into acceptable category (24.69%).
Interestingly, due to the specific property of THz image, the average pixel
intensity gave the best performance than the above complicated IQA algorithms,
with the PLCC, SROCC and RMSE of 0.9001, -0.8800 and 0.3857, respectively. This
study will help the users such as researchers or security staffs to obtain the
THz security images of good quality. Currently, our research group is
attempting to make this research more comprehensive.Comment: 13 pages, 8 figures, 4 table
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Subjective and objective quality evaluation of synthetic and high dynamic range images
Recent years have seen a huge growth in the acquisition, transmission, and storage of videos. The visual data consists of both natural scenes as well as synthetic scenes, such as animated movies, cartoons and video games. In all these cases, the ultimate goal is to provide the viewers with a satisfactory quality-of-experience. In addition to the traditional 8-bit images, high dynamic range imaging is also becoming popular because of its ability to represent the real world luminances more realistically. Coming up with objective image quality assessment algorithms for these applications is an interesting research problem. In this work, I have developed a synthetic image quality database by introducing varying degrees of different types of distortions and conducted a subjective experiment in order to obtain the ground-truth data. I evaluated the performance of state-of-the-art image quality assessment algorithms (typically meant for natural images) on this database, especially no-reference algorithms that have not been applied to the domain of computer graphics images before. I identified the top-performing algorithms along with analyzing the types of distortions on which the present algorithms show a less impressive performance. For high dynamic range(HDR) images, I have designed two new full-reference image quality assessment algorithms to judge the quality of tonemapped HDR images using statistical features extracted from them. I have also conducted a massive online crowd-sourced subjective test for HDR image artifacts arising from tonemapping, multiple-exposure fusion and post processing. To the best of our knowledge, presently this is the largest HDR image database in the world involving the largest number of source images and most number of human evaluations. Based on the subjective evaluations obtained, I have also proposed machine learning based no-reference image quality assessment algorithms to predict the perceptual quality of HDR images.Electrical and Computer Engineerin
Deep CNN Model for Non-Screen Content and Screen Content Image Quality Assessment
In the current world, user experience in various platforms matters a lot for different organizations. But providing a better experience can be challenging if the multimedia content on online platforms is having different kinds of distortions which impact the overall experience of the user. There can be various reasons behind distortions such as compression or minimal lighting condition while taking photos. In this work, a deep CNN-based Non-Screen Content and Screen Content NR-IQA framework is proposed which solves this issue in a more effective way. The framework is known as DNSSCIQ. Two different architectures are proposed based upon the input image type whether the input is a screen content or non-screen content image. This work attempts to solve this by evaluating the quality of such image
A New Image Quality Database for Multiple Industrial Processes
Recent years have witnessed a broader range of applications of image
processing technologies in multiple industrial processes, such as smoke
detection, security monitoring, and workpiece inspection. Different kinds of
distortion types and levels must be introduced into an image during the
processes of acquisition, compression, transmission, storage, and display,
which might heavily degrade the image quality and thus strongly reduce the
final display effect and clarity. To verify the reliability of existing image
quality assessment methods, we establish a new industrial process image
database (IPID), which contains 3000 distorted images generated by applying
different levels of distortion types to each of the 50 source images. We
conduct the subjective test on the aforementioned 3000 images to collect their
subjective quality ratings in a well-suited laboratory environment. Finally, we
perform comparison experiments on IPID database to investigate the performance
of some objective image quality assessment algorithms. The experimental results
show that the state-of-the-art image quality assessment methods have difficulty
in predicting the quality of images that contain multiple distortion types
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