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In the domain of image and multimedia processing, image quality is a critical factor, as it directly influences the performance of subsequent tasks such as compression, transmission, and content analysis. Reliable assessment of image quality is therefore essential not only for benchmarking algorithms but also for ensuring user satisfaction in real-world multimedia applications. The most advanced Blind image quality assessment (BIQA) methods are typically built upon deep learning models and rely on complex architectures that, while effective, require substantial computational resources and large-scale training datasets. This complexity can limit their scalability and practical deployment, particularly in resource-constrained environments. In this paper, we revisit a model inspired by one of the early applications of convolutional neural networks (CNNs) in BIQA and demonstrate that by leveraging recent advancements in machine learning—such as Bayesian hyperparameter optimization and widely used stochastic optimization methods (e.g., Adam)—it is possible to achieve competitive performance using a simpler, more scalable, and lightweight architecture. To evaluate the proposed approach, we conducted extensive experiments on widely used benchmark datasets, including TID2013 and KADID-10k. The results show that the proposed model achieves competitive performance while maintaining a substantially more efficient design. These findings suggest that lightweight CNN-based models, when combined with modern optimization strategies, can serve as a viable alternative to more elaborate frameworks, offering an improved balance between accuracy, efficiency, and scalability.

We propose three methods for the color quantization of superpixel images. Prior to the application of each method, the target image is first segmented into a finite number of superpixels by grouping the pixels that are similar in color. The color of a superpixel is given by the arithmetic mean of the colors of all constituent pixels. Following this, the superpixels are quantized using common splitting or clustering methods, such as median cut, k-means, and fuzzy c-means. In this manner, a color palette is generated while the original pixel image undergoes color mapping. The effectiveness of each proposed superpixel method is validated via experimentation using different color images. We compare the proposed methods with state-of-the-art color quantization methods. The results show significantly decreased computation time along with high quality of the quantized images. However, a multi-index evaluation process shows that the image quality is slightly worse than that obtained via pixel methods.

Nature-inspired artificial intelligence algorithms have been applied to color image quantization (CIQ) for some time. Among these algorithms, the particle swarm optimization algorithm (PSO-CIQ) and its numerous modifications are important in CIQ. In this article, the usefulness of such a modification, labeled IDE-PSO-CIQ and additionally using the idea of individual difference evolution based on the emotional states of particles, is tested. The superiority of this algorithm over the PSO-CIQ algorithm was demonstrated using a set of quality indices based on pixels, patches, and superpixels. Furthermore, both algorithms studied were applied to superpixel versions of quantized images, creating color palettes in much less time. A heuristic method was proposed to select the number of superpixels, depending on the size of the palette. The effectiveness of the proposed algorithms was experimentally verified on a set of benchmark color images. The results obtained from the computational experiments indicate a multiple reduction in computation time for the superpixel methods while maintaining the high quality of the output quantized images, slightly inferior to that obtained with the pixel methods.

We present analysis of the RR Lyrae star, LS Her, and confirm the previously reported modulation to its Blazhko cycles. We performed Fourier analysis on two sectors (Sector 24 and 25) of data from the Transiting Exoplanet Survey Satellite (TESS) spanning 53 days. We find LS Her to have a primary pulsation period of 0.2308 day and a Blazhko period of 12.7 days in keeping with previously reported results. We also identified sideband frequencies around the Blazhko multiplets suggesting the Blazhko cycle is modulated on a timescale of 112 days. Analysis of the Blazhko effect using the TESS data clearly shows a changing amplitude and phase throughout the four Blazhko cycles. We compared our modeled results, which were based on our TESS frequency analysis, to TESS data (Sector 51) taken ∼700 days later and found our modulation model was not a good representation of the data. We then coupled our TESS analysis with the modulation frequency results from Wils et al. and found excellent agreement with the Sector 51 data. To further test this result we obtained ground-based, V-magnitude observations of LS Her in the summer of 2022. This data also showed excellent agreement with our coupled modulation model. We have verified that LS Her is a Blazhko star with a modulated Blazhko period of 109 days, stability over the 862 days of observations, and possible stability lasting over 15 yr. We discuss the ramifications of the modulation for other Blazhko stars that show Blazhko effect changes over time.

KHM clustering technique as a segmentation method for endoscopic colour images In this paper, the idea of applying the [k]-harmonic means (KHM) technique in biomedical colour image segmentation is presented. The [k]-means (KM) technique establishes a background for the comparison of clustering techniques. Two original initialization methods for both clustering techniques and two evaluation functions are described. The proposed method of colour image segmentation is completed by a postprocessing procedure. Experimental tests realized on real endoscopic colour images show the superiority of KHM over KM.

Objective image quality assessment (IQA) measures are playing an increasingly important role in the evaluation of digital image quality. New IQA indices are expected to be strongly correlated with subjective observer evaluations expressed by Mean Opinion Score (MOS) or Difference Mean Opinion Score (DMOS). One such recently proposed index is the SuperPixel-based SIMilarity (SPSIM) index, which uses superpixel patches instead of a rectangular pixel grid. The authors of this paper have proposed three modifications to the SPSIM index. For this purpose, the color space used by SPSIM was changed and the way SPSIM determines similarity maps was modified using methods derived from an algorithm for computing the Mean Deviation Similarity Index (MDSI). The third modification was a combination of the first two. These three new quality indices were used in the assessment process. The experimental results obtained for many color images from five image databases demonstrated the advantages of the proposed SPSIM modifications.

To date, hundreds of millions tons of plastics has been produced worldwide. Their production and disposal are associated with high pollution and carbon release into the atmosphere. A more environmentally friendly alternative is bioplastics, and the most popular is polyhydroxybutyrate (PHB) polymer. Large amounts of PHB can be obtained from activated sludge where used cooking oil or other industrial waste can be used as potential substrates. In this work, efficient bioplastic production strategies are studied, and the considered substrate is a mixture of oil and peptone. Pseudomonas fluorescens bacteria are used to accumulate PHB, and the cultivation of microorganisms is carried out in batch and continuous-flow bioreactors. Microscopic observations and laboratory essays are performed to confirm presence of PHB and other key parameters. The obtained results allow us to determine the optimal feeding strategy.

In recent years, many new metrics highly correlated with the Mean Opinion Score (MOS) have been proposed for assessing image quality through Full-Reference Image Quality Assessment (FR-IQA) methods, such as MDSI, HPSI, and GMSD. Eight of these selected metrics, which compare reference and distorted images in a symmetrical manner, are briefly described in this article, and their performance is evaluated using correlation criteria (PLCC, SROCC, and KROCC), as well as RMSE. The aim of this paper is to develop a new, efficient quality index based on a combination of several high-performance metrics already utilized in the field of Image Quality Assessment (IQA). The study was conducted on four benchmark image databases (TID2008, TID2013, KADID-10k, and PIPAL) and identified the three best-performing metrics for each database. The paper introduces a New Combined Metric (NCM), which is a weighted sum of three component metrics, and demonstrates its superiority over each of its component metrics across all the examined databases. An optimization method for determining the weights of the NCM is also presented. Additionally, an alternative version of the combined metric, based on the fastest metrics and employing symmetric calculations for pairs of compared images, is discussed. This version also demonstrates strong performance.

Color image quantization has become an important operation often used in tasks of color image processing. There is a need for quantization methods that are fast and at the same time generating high quality quantized images. This paper presents such color quantization method based on downsampling of original image and K-Means clustering on a downsampled image. The nearest neighbor interpolation was used in the downsampling process and Wu’s algorithm was applied for deterministic initialization of K-Means. Comparisons with other methods based on a limited sample of pixels (coreset-based algorithm) showed an advantage of the proposed method. This method significantly accelerated the color quantization without noticeable loss of image quality. The experimental results obtained on 24 color images from the Kodak image dataset demonstrated the advantages of the proposed method. Three quality indices (MSE, DSCSI and HPSI) were used in the assessment process.

We present analysis of the RR Lyrae star, LS Her and confirm the previously reported modulation to its Blazhko cycles. We performed Fourier analysis on two sectors (Sector 24 & 25) of data from the Transiting Exoplanet Survey Satellite (TESS) spanning 53 days. We find LS Her to have a primary pulsation period of 0.2308 d and a Blazhko period of 12.7 d in keeping with previously reported results. We also identified side-band frequencies around the Blazhko multiplets suggesting the Blazhko cycle is modulated on a time scale of 112 days. Analysis of the Blazhko effect using the TESS data clearly shows a changing amplitude and phase throughout the four Blazhko cycles. We compared our modeled results, which were based on our TESS frequency analysis, to TESS data (Sector 51) taken ~700 days later and found our modulation model was not a good representation of the data. We then coupled our TESS analysis with the modulation frequency results from Wils et al. (MNRAS 387 (2008) 783-787) and found excellent agreement with the Sector 51 data. To further test this result we obtained ground-based, V-magnitude observations of LS Her in the summer of 2022. This data also showed excellent agreement with our coupled modulation model. We have verified that LS Her is a Blazhko star with a modulated Blazhko period of 109 days, stability over the 862 days of observations, and possible stability lasting over 15 years. We discuss the ramifications of the modulation for other Blazhko stars that show Blazhko effect changes over time.