Objective image quality assessment (IQA) aims to predict human perceptual judgments, a task increasingly challenging with novel imaging modalities like light fields. Unlike traditional 2D images, light fields enable diverse viewing experiences across various displays (traditional, stereoscopic, head-mounted) and levels of interactivity, introducing significant contextual variability in perception—a concept we term Situational Quality. This paper investigates how such evaluation contexts shape perceived image quality and challenges objective metrics, especially for complex visual data. Our analysis of three distinct subjective quality studies using data from the same dataset, using varying methodologies and simulated viewing contexts, indicates significant variations in subjective outcomes. To assess the implications for objective IQA, we trained the same learning-based metric (a U-Net predicting weighted PSNR masks) independently on the results from each subjective context. Our experimental analysis demonstrates that metric performance varies across contexts. Crucially, the learned masks, which represent the metric’s internal focus, differ significantly. These findings underscore a fundamental limitation for context-blind objective metrics: they are particularly ill-equipped to handle the inherent perceptual flexibility and diverse viewing scenarios of novel modalities like light fields. We highlight the critical need for future IQA research, especially for advanced imaging, to account for situational factors.

Tone-mapping is a crucial step in the task towards displaying high dynamic range (HDR) images on standard displays. Given the number of possible ways to tone-map such images, development of an objective quality criterion, enabling selection of the most suitable tone-mapping operator (TMO) and setting its parameters in order to maximize the quality of the reproduction, is of high interest. In this paper, a new objective metric for natural tone-mapped images is proposed. It is based on a fusion of several perceptually relevant features that have been carefully selected using an appropriate feature selection procedure. The outcome of the selection also provides a valuable insight into the importance of particular perceptual aspects when judging the quality of tone-mapped HDR content. The performance of the resulting combination of features is thoroughly evaluated with respect to three publicly available databases and compared to several relevant state-of-the-art criteria. The proposed approach is shown to significantly outperform the tested metrics and can, therefore, be considered a competitive alternative for tone-mapped images evaluation.

Most of the effort in image quality assessment (QA) has been so far dedicated to the degradation of the image. However, there are also many algorithms in the image processing chain that can enhance the quality of an input image. These include procedures for contrast enhancement, deblurring, sharpening, up-sampling, denoising, transfer function compensation, etc. In this work, possible strategies for the quality assessment of sharpened images are investigated. This task is not trivial because the sharpening techniques can increase the perceived quality, as well as introduce artifacts leading to the quality drop (over-sharpening). Here, the framework specifically adapted for the quality assessment of sharpened images and objective metrics comparison in this context is introduced. However, the framework can be adopted in other quality assessment areas as well. The problem of selecting the correct procedure for subjective evaluation was addressed and a subjective test on blurred, sharpened, and over-sharpened images was performed in order to demonstrate the use of the framework. The obtained ground-truth data were used for testing the suitability of state-ofthe- art objective quality metrics for the assessment of sharpened images. The comparison was performed by novel procedure using ROC analyses which is found more appropriate for the task than standard methods. Furthermore, seven possible augmentations of the no-reference S3 metric adapted for sharpened images are proposed. The performance of the metric is significantly improved and also superior over the rest of the tested quality criteria with respect to the subjective data.

There are several standard methods for evaluating the performance of models for objective quality assessment with respect to results of subjective tests. However, all of them suffer from one or more of the following drawbacks: They do not consider the uncertainty in the subjective scores, requiring the models to make certain decision where the correct behavior is not known. They are vulnerable to the quality range of the stimuli in the experiments. In order to compare the models, they require a mapping of predicted values to the subjective scores, thus not comparing the models exactly as they are used in the real scenarios. In this paper, new methodology for objective models performance evaluation is proposed. The method is based on determining the classification abilities of the models considering two scenarios inspired by the real applications. It does not suffer from the previously stated drawbacks and enables to easily evaluate the performance on the data from multiple subjective experiments. Moreover, techniques to determine statistical significance of the performance differences are suggested. The proposed framework is tested on several selected metrics and datasets, showing the ability to provide a complementary information about the models' behavior while being in parallel with other state-of-the-art methods.

UNLABELLED: SIMToolbox is an open-source, modular set of functions for MATLAB equipped with a user-friendly graphical interface and designed for processing two-dimensional and three-dimensional data acquired by structured illumination microscopy (SIM). Both optical sectioning and super-resolution applications are supported. The software is also capable of maximum a posteriori probability image estimation (MAP-SIM), an alternative method for reconstruction of structured illumination images. MAP-SIM can potentially reduce reconstruction artifacts, which commonly occur due to refractive index mismatch within the sample and to imperfections in the illumination. AVAILABILITY AND IMPLEMENTATION: SIMToolbox, example data and the online documentation are freely accessible at http://mmtg.fel.cvut.cz/SIMToolbox. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

We propose a novel binarization method based on a signal reconstruction using an iterative detection network. The algorithm simulates the whole image acquisition process taking into account a point spread function of the imaging system and its noise characteristics. The negative influence of image blur and noise is effectively suppressed by iterative detection network based on the criterion of maximum a posteriori probability. The proposed method was successfully applied to noisy microscopy images. Experiments show that the proposed method due to the noise suppression and deconvolution properties provides for noisy images significantly better results compared to common thresholding techniques. Binarized images obtained by the proposed method can be particularly useful for particle detection and analysis of cell samples.

The upcoming JPEG XT is under development for High Dynamic Range (HDR) image compression. This standard encodes a Low Dynamic Range (LDR) version of the HDR image generated by a Tone-Mapping Operator (TMO) using the conventional JPEG coding as a base layer and encodes the extra HDR information in a residual layer. This paper studies the performance of the three profiles of JPEG XT (referred to as profiles A, B and C) using a test set of six HDR images. Four TMO techniques were used for the base layer image generation to assess the influence of the TMOs on the performance of JPEG XT profiles. Then, the HDR images were coded with different quality levels for the base layer and for the residual layer. The performance of each profile was evaluated using Signal to Noise Ratio (SNR), Feature SIMilarity Index (FSIM), Root Mean Square Error (RMSE), and CIEDE2000 color difference objective metrics. The evaluation results demonstrate that profiles A and B lead to similar saturation of quality at the higher bit rates, while profile C exhibits no saturation. Profiles B and C appear to be more dependent on TMOs used for the base layer compared to profile A.

The delivery of multimedia content over HTTP and on top of existing Internet infrastructures is becoming the preferred method within heterogeneous environment. The basic design principle is having an intelligent client which selects given and applicable media representations by issuing HTTP requests for individual segments based on the users' context and current conditions. Typically, this client behavior differs between implementations of the same kind and for the objective evaluations thereof appropriate datasets are needed. This paper presents a distributed dataset for the recently published MPEG-DASH standard which is mirrored at different sites across Europe, namely Klagenfurt, Paris, and Prague. A client implementation may choose to request segments from these sites and dynamically switch to a different location, e.g., in case the one currently used causes any issues. Thus, this distributed DASH dataset can be used for real-world evaluations enabling the simulation of switching between different content delivery networks.