2,273 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
Regression-free Blind Image Quality Assessment
Regression-based blind image quality assessment (IQA) models are susceptible
to biased training samples, leading to a biased estimation of model parameters.
To mitigate this issue, we propose a regression-free framework for image
quality evaluation, which is founded upon retrieving similar instances by
incorporating semantic and distortion features. The motivation behind this
approach is rooted in the observation that the human visual system (HVS) has
analogous visual responses to semantically similar image contents degraded by
the same distortion. The proposed framework comprises two classification-based
modules: semantic-based classification (SC) module and distortion-based
classification (DC) module. Given a test image and an IQA database, the SC
module retrieves multiple pristine images based on semantic similarity. The DC
module then retrieves instances based on distortion similarity from the
distorted images that correspond to each retrieved pristine image. Finally, the
predicted quality score is derived by aggregating the subjective quality scores
of multiple retrieved instances. Experimental results on four benchmark
databases validate that the proposed model can remarkably outperform the
state-of-the-art regression-based models.Comment: 11 pages, 7 figures, 50 conference
Full-Reference Image Quality Expression via Genetic Programming
Bakurov, I., Buzzelli, M., Schettini, R., Castelli, M., & Vanneschi, L. (2023). Full-Reference Image Quality Expression via Genetic Programming. IEEE Transactions on Image Processing, 32, 1458-1473. https://doi.org/10.1109/TIP.2023.3244662
This work was supported by national funds through the FCT (Fundação para a Ciência e a Tecnologia) under the projects Algoritmos de Inteligência artificial no Consumo de crédito e conciliação de Endividamento (AICE) (DSAIPA/DS/0113/2019) and UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS. Mauro Castelli acknowledges the financial support from the Slovenian Research Agency (research core funding no. P5-0410).Full-reference image quality measures are a fundamental tool to approximate the human visual system in various applications for digital data management: from retrieval to compression to detection of unauthorized uses. Inspired by both the effectiveness and the simplicity of hand-crafted Structural Similarity Index Measure (SSIM), in this work, we present a framework for the formulation of SSIM-like image quality measures through genetic programming. We explore different terminal sets, defined from the building blocks of structural similarity at different levels of abstraction, and we propose a two-stage genetic optimization that exploits hoist mutation to constrain the complexity of the solutions. Our optimized measures are selected through a cross-dataset validation procedure, which results in superior performance against different versions of structural similarity, measured as correlation with human mean opinion scores. We also demonstrate how, by tuning on specific datasets, it is possible to obtain solutions that are competitive with (or even outperform) more complex image quality measures.authorsversionauthorsversionpublishe
Retrieval of ice-nucleating particle concentrations from lidar observations and comparison with UAV in situ measurements
Aerosols that are efficient ice-nucleating particles (INPs) are crucial for the formation of cloud ice via heterogeneous nucleation in the atmosphere. The distribution of INPs on a large spatial scale and as a function of height determines their impact on clouds and climate. However, in situ measurements of INPs provide sparse coverage over space and time. A promising approach to address this gap is to retrieve INP concentration profiles by combining particle concentration profiles derived by lidar measurements with INP efficiency parameterizations for different freezing mechanisms (immersion freezing, deposition nucleation). Here, we assess the feasibility of this new method for both ground-based and spaceborne lidar measurements, using in situ observations collected with unmanned aerial vehicles (UAVs) and subsequently analyzed with the FRIDGE (FRankfurt Ice nucleation Deposition freezinG Experiment) INP counter from an experimental campaign at Cyprus in April 2016. Analyzing five case studies we calculated the cloud-relevant particle number concentrations using lidar measurements (n250,dry with an uncertainty of 20 % to 40 % and Sdry with an uncertainty of 30 % to 50 %), and we assessed the suitability of the different INP parameterizations with respect to the temperature range and the type of particles considered. Specifically, our analysis suggests that our calculations using the parameterization of Ullrich et al. (2017) (applicable for the temperature range −50 to −33 ∘C) agree within 1 order of magnitude with the in situ observations of nINP; thus, the parameterization of Ullrich et al. (2017) can efficiently address the deposition nucleation pathway in dust-dominated environments. Additionally, our calculations using the combination of the parameterizations of DeMott et al. (2015, 2010) (applicable for the temperature range −35 to −9 ∘C) agree within 2 orders of magnitude with the in situ observations of INP concentrations (nINP) and can thus efficiently address the immersion/condensation pathway of dust and nondust particles. The same conclusion is derived from the compilation of the parameterizations of DeMott et al. (2015) for dust and Ullrich et al. (2017) for soot.Peer reviewe
Digital Image Access & Retrieval
The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio
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