Color Space Research Articles

Exploring the impact of logo colors on the perceived tastes of tea beverages—A study on hand-shaken beverages in Taiwan

Color visually communicates the product’s flavors to consumers and further influences their taste perception. This study explores the perceived taste of tea beverages caused by the logo’s principal colors, using hand-shaken tea beverages in Taiwan as an example. To identify the linkage between the logo color and tea tastes, this study divides the taste of tea beverages into four categories: sweetness, freshness, bitterness, and astringency. Then, the 69 tea beverage logos are allocated into the 14 color sections in the CIELAB color space according to their primary colors. The Correspondence Analysis method is employed to visualize the relationships between the logos and the perceived tastes. The tea tastes are then mapped into the color sections in the CIELAB color space. The analysis results reveal that the sweetness links to logos in the Warm Scheme colors (hue angle from 0 to 59 degrees). The fresh taste is bound with the logo with the Cool White Scheme colors (hue angle from 90 to 149 degrees and brightness >80). Finally, the bitter and astringent tastes link to the logo colors in the Cold Black Scheme colors (hue angle from 60 to 89 degrees, 150 to 329 degrees, and brightness <25). This study expands the color and taste association literature from general food to tea beverages. Our obtained empirical results can be applied to hand-shaken beverage companies to select principal colors for designing logos and packages that align with tea beverages’ perceived tastes to convey brand recognition accurately.

Machine learning with analysis-of-variance-based method for identifying rice varieties

Rice, which has the highest production and consumption rates worldwide, is one of the main nutrients in our country because it is economical and nutritious. Rice undergoes various stages of production from the field to the dinner tables, with the cleaning phase involving separation of rice from unwanted materials. There are different varieties of rice such as arborio, basmati, aracadaga, ipsala, and jasmine. Therefore, it is necessary to identify rice varieties based on customer demands. Rice variety identification is primarily based on its outer appearance, including color, size, and shape. In this study, 75,000 rice grain images, including 15,000 images for each variety, were considered. Using a combination of 12 morphological features, four shape features, and 90 color features obtained from five different color spaces, 106 features were extracted from the images. An analysis of variance (ANOVA) was employed to select high-rank features, which were then fed to a support vector machine (SVM) for classification. The experimental results demonstrated that the utilization of 40 high-rank features yielded impressive outcomes, with a validation accuracy of 99.89 %, cost value of 16, test accuracy of 99.81 %, and cost value of 28. These high accuracy rates are particularly noteworthy, as they indicate the robustness and reliability of the proposed methodology in distinguishing between different rice varieties with minimal error. This high level of precision is critical for practical applications, such as quality control in rice production, to ensure that customers receive the specific rice variety they demand. Implementing this methodology in real-world scenarios could significantly enhance the efficiency and accuracy of the rice variety identification processes, benefiting both producers and consumers.

Color Analysis of Brocade from the 4th to 8th Centuries Driven by Image-Based Matching Network Modeling

To achieve the color matching rules for the textiles discovered during Silk Road excavations between the 4th and 8th centuries, this research proposed an image-based matching network modeling method. The Silk Road facilitated trade and cultural exchange between the East and West, and the textiles found along the way depict the development of fabrics in a color scheme with great cultural significance. A total of 165 images with brocade patterns were collected from a book with a detailed description of the Western influences on textiles along the Silk Road. Two different clustering methods, including the K-means clustering method and octree quantization approach, were used to extract the primary and secondary colors. By combining the HSV color space with the PCCS color system, the color distribution was analyzed to discover the features of representative color patterns. The co-occurrence relationship of the auxiliary colors was explored using the Apriori algorithm, and a total of eight association rules were established. The results showed that the K-means clustering algorithm can show a better effect of color classification to obtain three primary colors and nine secondary colors. The matching mechanism with a visualized network model was also proposed, which showed that reddish-yellow tones are the main colors in the brocade patterns, and the light and soft tones separately account for 27% and 20%. Beige and brown are the most common colorways, with a confidence level of 47%. One style of brocade pattern was used to demonstrate different appearances within various color networks, which could be applied to 3D virtual fitting. This image-based matching network modeling approach makes the color matching schemes visible, and can assist fashion design with fabric features influenced by historical and cultural development.

From color naming to color perception: Cross‐linguistic differences of the chromatic information processing in monolingual and bilingual speakers

AbstractThere has been long running debate about the interaction of language and perception. In this context, bilingual people have often shown benefits, due to their double‐active linguistic system, in cognitive functions, like inhibition, attention, and memory, which are central for visual perception. Color naming and categorization are domains for studying cross‐linguistic effects, which arise from conceptual and perceptual variations across speakers of different languages. In this study, we compared the ‘blue’ lexicon of highly proficient French‐Italian bilinguals to monolingual speakers of the corresponding languages. Prior studies have shown that Italian has two basic color terms for the blue area of color space: one denotes light blue hues (azzurro) and the other dark blue hues (blu), whilst French, with only one basic term bleu, lacks this distinction. We used a Stroop test to probe differences in perception and categorization of blue in bi‐ and monolingual speakers. We found that Italian monolinguals name the ink color more accurately and more rapidly when the word blu is rendered in dark blue ink (corresponding to the word blu) than when it is printed in light blue ink (corresponding to azzurro), since the latter represents an incongruent condition for them. This ‘category effect’ does not exist for French monolingual speakers. Our bilinguals' results demonstrate that, despite the emergence of a specific in‐between perceptual behavior, bilinguals generally performed like Italian monolinguals. These outcomes confirm the hypothesis that their second language categories (Italian) dominate their native language (French), attesting that lexical distinctions influence perceptual faculties in general. However, the ratio of the interference effect (longer reaction times for incongruent stimuli compared to control stimuli) and the facilitation effect (shorter reaction times for congruent stimuli compared to control stimuli) is not the same for bilinguals and monolinguals. The highest magnitude in the facilitation effect was revealed for bilinguals, whereas the highest magnitude in the interference effect was revealed for Italian monolingual speakers. This phenomenon adds evidence to the existence of enhanced bilingual cognitive control abilities.

Efficient Computation of Geodesics in Color Space.

Although scientists agree that a perceptual color space is not Euclidean and color difference measures, such as CIELAB's ∆E2000, model these aspects of color perception, colormaps are still mostly evaluated through piecewise linear interpolation in a Euclidean color space. In a non-Euclidean setting, the piecewise linear interpolation of a colormap through control points translates to finding shortest paths. Alternatively, a smooth interpolation can be generalized to finding the straightest path. Both approaches are difficult to solve and are compute intensive. We compare the 11 most promising optimization algorithms for the computation of a geodesic either as the shortest or as the straightest path to find the most efficient one to use for colormap interpolation in real-world applications. For two control points, the zero curvature algorithms excelled, especially the 2D relaxation method. For multiple control points, only the mimimal curvature algorithms can produce smooth curves, amongst which the 1D relaxation method performed best.

An underwater image enhancement method based on multi-scale layer decomposition and fusion

High-quality underwater images can intuitively reflect the most realistic underwater conditions, guiding for underwater environmental monitoring and resource exploration strongly. But when factors like light absorption affect underwater optical imaging, it has been found that poor visibility and blurred texture details occur in acquired images, posing challenges for the identification and detection of underwater targets. To obtain natural images, an enhancement algorithm is proposed based on multi-scale layer decomposition and fusion. The algorithm employs different strategies to recover image attenuation information from both local and global perspectives, generating two complementary preprocessed fusion inputs. For fusion input 1, operations are conducted in the RGB color space. Initially, the mean proportion of each color channel is used to identify the attenuated color channel. Then, a local compensation strategy is adaptively applied to restore the pixel intensity of the attenuated color channel. Finally, a statistical color correction method is used to eliminate color cast in the image. Fusion input 2 involves two processing stages. In the Lab color space, the algorithm uses the grayscale information to reduce the deviation in the mean values of channels a and b globally. The local mean information of the component L enhances detail textures. In the RGB color space, linear stretching is applied to correct color deviations. To fuse the structural features of two complementary preprocessed inputs and avoid interference between signals from different layers, the color channels of fused input image are first decomposed into muti-scale structural layers based on structural priors. Then, the image enhancement is achieved through layer-by-layer fusion of the corresponding color channels of the two inputs. By testing and analyzing with the, it was found that the proposed method can improve the clarity of attenuated images in various underwater scenarios of UIEBD and RUIE datasets effectively, enhancing image detail and texture richness, increases contrast, and achieving natural and comfortable visual quality. Compared with the quantitative metrics of 14 other algorithms, the proposed algorithm shows an average score improvement of 10.14, 90.48, and 2.06, respectively, in metrics AG (average gradient), EI (edge intensity), and NIQE (natural image quality evaluator). In the RUIE dataset, it shows an average score improvement of 10.21, 94.76, and 1.86, respectively.

Feature Fusion GAN Based Virtual Staining on Plant Microscopy Images.

Virtual staining of microscopy specimens using GAN-based methods could resolve critical concerns of manual staining process as displayed in recent studies on histopathology images. However, most of these works use basic-GAN framework ignoring microscopy image characteristics and their performance were evaluated based on structural and error statistics (SSIM and PSNR) between synthetic and ground-truth without considering any color space although virtual staining deals with color transformation. Besides, major aspects of staining, like color, contrast, focus, image-realness etc. were totally ignored. However, modifications of GAN architecture for virtual staining might be suitable by incorporating microscopy image features. Further, its successful implementation need to be examined by considering various aspects of staining process. Therefore, we designed, a new feature-fusion-GAN for virtual staining followed by performance assessment by framing a state-of-the-art multi-evaluation framework that includes numerous metrics in -qualitative (based on histogram-correlation of color and brightness); quantitative (SSIM and PSNR); focus aptitude (Brenner metrics and Spectral-Moments); and influence on perception (semantic perceptual influence score). For, experimental validation cell boundaries were highlighted by two different staining reagents, Safranin-O and Toluidine-Blue-O on plant microscopy images of potato tuber. We evaluated virtually stained image quality w.r.t ground-truth in RGB and YCbCr color spaces based on defined metrics and results are found very consistent. Further, impact of feature fusion has been demonstrated. Collectively, this study could be a baseline towards guiding architectural upgrading of deep pipelines for virtual staining of diverse microscopy modalities followed by future benchmark methodology or protocols.

Impact of blur on clinical and occupational colour vision test results.

To evaluate whether colour vision normal (CVN) adults pass two Fletcher-Evans (CAM) lantern tests and to investigate the impact of imposed blur on Ishihara, CAM lantern and computerised colour discrimination test (colour assessment and diagnosis test [CAD] and Cambridge colour test [CCT]) results. In a pilot experiment, 20 (16 CVN and 4 colour vision deficient [CVD]) participants with normal VA were tested with the CAM lantern. In the main experiment, the impact of imposed dioptric blur (up to +8.00 D) on visual acuity and the Ishihara test, CAM lantern, CAD and CCT was assessed for 15 CVN participants. CVN participants can fail the CAM lantern, with specificity of 81.25% (aviation mode) and 75% (clinical mode), despite following the test requirements of participants having at least 0.18 logMAR (6/9) in the better eye. With blur, test accuracy was affected. As expected, significant detrimental effects of blur on test results were found for logMAR VA and CAM lantern (aviation) with +1.00 D or higher. Ishihara, CAD and CCT results were not detrimentally affected until +8.00 D. Yellow-blue discrimination was more affected by blur for the CAD than the CCT, which was not explained by the different colour spaces used or vectors tested. False-positive findings on lantern colour vision tests with small apertures are likely to be increased in patients with blur due to uncorrected refractive error or ocular and visual pathway disease. Other colour vision tests with larger stimuli are more robust to blur.

Effect of the addition of different forms of rosemary (Rosmarinus officinalis L.) on the quality of vacuum-packed minced pork

Abstract Introduction Apart from their antioxidant activity, plant-derived bioactive compounds can also positively affect the quality of meat and meat products by improving their sensory and microbiological properties and preventing discolouration. The aim of this study was to determine how the addition of different forms of rosemary improved the quality of pork. Material and Methods Minced pork samples were divided into a control sample without additives (C) and three experimental samples with certified additives (15 mg/kg of meat each), i.e. rosemary oleoresin (ROL), extract (REX) and essential oil (REO). Each was further divided into three subsamples; the first was evaluated before storage, and the second and third were evaluated after respective 7- and 14-day vacuum-packed storage at 2°C. The TBARS value was expressed as mg of malondialdehyde (MDA) per kg of meat. Colour was determined based on the values of the standard colour space values of L* (lightness), a* (redness) and b* (yellowness) as well as C* (chroma) and h° (hue angle). Sensory attributes of the samples were evaluated on a nine-point scale. The pour-plating procedure was used for the enumeration of Pseudomonas, mesophilic lactic acid bacteria, psychrotrophic bacteria and rods of the Enterobacteriaceae family. Within each bacterial group, the most common colonies were identified by matrix-assisted laser desorption and ionization (MALDI). Results Lipid oxidation was most effectively inhibited by REO. The addition of ROL and REO to pork lightened its colour. Meat with REO had stronger redness, whereas meat with ROL had stronger yellowness. The addition of REX affected the sensory properties of pork most beneficially. Neither Enterobacteriaceae nor Pseudomonas spp. were detected in REO pork, which also contained lower counts of lactic acid bacteria than group C pork. Conclusion The results of this study indicate that rosemary has antioxidant and antimicrobial properties, and may improve the colour and sensory attributes of pork. The effect exerted by rosemary on meat quality may vary depending on the physical form of the additive.

What Color Is That Red Dress?

AbstractNow it is possible to apply color space and visual accuracy workflow advancements—from virtual production to post‐production to architectural‐scale signage applications.

Wearable photonic crystal double network hydrogel sensor based on structural color analysis

Abstract This paper introduces a high-toughness photonic crystal (PhCs) dual-network hydrogel sensor designed for mechanical sensing, which enables quantitative analysis of structural color using the HSB color space. The hydrogel achieves a maximum tensile strain of 250% under a tensile stress of 3.5 MPa, thanks to its dual-network structure comprising a covalently cross-linked polyacrylamide (PAM) network and an ionically cross-linked sodium alginate (SA) network. At an 80% tensile strain, the PAM-SA photonic crystal hydrogel displays a blue shift in the bandgap wavelength of over 130 nm and demonstrates a sensitivity of 1.69 nm/%. The analysis of the force-induced color change in the PAM-SA photonic crystal hydrogel utilized both RGB and HSB color spaces. In the HSB color space, the hue component (H) exhibited a strong linear correlation with strain (R2>0.95), indicating the feasibility of quantitative structural color analysis using HSB. As a wearable sensor, the PAM-SA photonic crystal hydrogel precisely detects human motion via bandgap displacement (R2=0.989) and structural color change (R2=0.978). The PAM-SA PhCs hydrogel, featuring easily accessible color information and high sensitivity, has broad potential applications in wearable devices and mechanical sensors.

A 3D U-NET BASED ON EARLY FUSION MODEL: IMPROVEMENT, COMPARATIVE ANALYSIS WITH STATE-OF-THE-ART MODELS AND FINE-TUNING

Multi-organ segmentation is the process of identifying and separating multiple organs in medical images. This segmentation allows for the detection of structural abnormalities by examining the morphological structure of organs. Carrying out the process quickly and precisely has become an important issue in today's conditions. In recent years, researchers have used various technologies for the automatic segmentation of multiple organs. In this study, improvements were made to increase the multi-organ segmentation performance of the 3D U-Net based fusion model combining HSV and grayscale color spaces and compared with state-of-the-art models. Training and testing were performed on the MICCAI 2015 dataset published at Vanderbilt University, which contains 3D abdominal CT images in NIfTI format. The model's performance was evaluated using the Dice similarity coefficient. In the tests, the liver organ showed the highest Dice score. Considering the average Dice score of all organs, and comparing it with other models, it has been observed that the fusion approach model yields promising results.

Accuracy of Color Determination by Intraoral Scanners in Bleached and Unbleached Teeth: A Clinical Study.

This clinical study aimed to evaluate the color measurement ability of intraoral scanners (Trios 3, (3Shape, Copenhagen, Denmark) and Cerec Omnicam, (Dentsply Sirona, Charlotte, NC, USA) in comparison to VITA Classical and VITA 3D-Master shades obtained by a spectrophotometer (Easyshade V, VITA, Zahnfabrik, Bad Säckingen, Germany). Four hundred eighty teeth from thirty patients were included in the study. Fifteen patients had never undergone bleaching, and the other fifteen had undergone professional bleaching within the last 6 months. VITA Classical and VITA 3D Master shades were determined using Trios 3 (T3), Cerec Omnicam (CO), and EasyShade V from cervical, middle, and incisal/occlusal areas of maxillary and mandibular central and lateral incisors, canines, and first premolars. Color accuracy rates (cAR) were assessed by categorizing the color data according to the Munsell color system for bleached and unbleached teeth, maxillary and mandibular, and teeth arranged from anterior to posterior in each quadrant. McNemar, Mann-Whitney U, and Wilcoxon tests and Point Biserial Correlation Analysis were used for statistical analysis (significance level α=0.050). The bleached teeth had a significantly higher cAR for T3 in VITA 3D Master and VITA Classical guides (p<0.05). The cAR of the incisal area in VITA 3D Master was significantly lower in both scanners (p<0.05) except for the lightness/hue/chroma of bleached teeth. T3 cAR increased statistically significantly from anterior to posterior (p<0.001); however, no significant difference was found for CO (p=0.482). The color accuracy rates of both scanners for shade guides that included all color components in this study were below 30%, which was not clinically satisfactory for color measurement. Confirming the measurements with a spectrophotometer is recommended.

MACHINE LEARNING ALGORITHM FOR RAPID OBJECT DETECTION BASED ON COLOR FEATURES

The identification of objects is of utmost importance in a wide range of computer vision applications, such as surveillance systems, autonomous cars, and environmental monitoring. Accurate and efficient object recognition methods are crucial in pastoral environments, characterized by the prominent presence of cattle and other objects, to provide effective analysis and decision-making processes. The present study paper introduces an innovative methodology for efficient identification of objects in pastoral landscapes through the utilization of a Colour Feature Extreme Learning Machine (CF-ELM). The CF-ELM method integrates color characteristics with the ELM algorithm to attain enhanced object detection accuracy while preserving computational economy. The experimental findings provide empirical evidence supporting the efficacy and efficiency of the suggested approach in the detection of items within pastoral landscapes. In addition to the CF-ELM, an algorithm for desktop-based categorization of items within a pastoral environment is provided, with individual speeds ranging from 0.05 s to 0.17 s for a single image, evaluated in each color space. The algorithm is intended for usage in scenarios with challenging and variable terrain, making it appropriate for application in agricultural or pastoral settings.

Detection and Classification of Banana Leaf Diseases: Systematic Literature Review

Bananas, a staple fruit globally, are essential for sustenance, employment, and income. However, diseases like Sigatoka, Bacterial Wilt, Bunchy Top, and Fusarium Wilt pose a threat to their cultivation, affecting both small-scale and large-scale production. This survey investigates methods for the early identification and classification of these banana leaf diseases using deep learning and machine learning techniques. A systematic review of 15 studies revealed that the majority of research concentrates on binary classification, which distinguishes healthy from diseased leaves. Common preprocessing steps include image resizing, color space conversion, and background removal to improve model accuracy. We utilize techniques such as ensemble approaches, support vector machines (SVM), random forests, K-means clustering, and convolutional neural networks (CNNs), with CNNs demonstrating superior performance, achieving accuracy rates ranging from 85% to 98.97%. CNNs excel in hierarchical feature extraction but require significant computational power. Traditional machine learning methods offer simplicity and resistance to overfitting but need careful parameter tuning. Advanced deep learning architectures, such as DenseNet and Inception V3, achieve high accuracy but with greater computational demands. Lightweight models like SqueezeNet balance performance and size, but ensemble methods, while improving generalization, add complexity. The choice of method depends on dataset characteristics, available computational resources, and desired trade-offs between performance and complexity. This study provides an overview of current research in banana leaf disease classification, discussing the strengths and limitations of various approaches and suggesting directions for future research to improve detection accuracy and robustness.

Estimating SPAD, Nitrogen Concentration, and Chlorophyll Content in Rice Leaves using Calibrated Smartphone Digital Image

Laboratory analysis is commonly used to determine nitrogen and chlorophyll content. However, smartphones can serve as rapid, mobile, and non-destructive tools for this purpose. An equation can be created to calculate nitrogen and chlorophyll content by analyzing color parameters from digital images of rice leaves. An examination was performed on 86 rice leaf samples from the maximum tillering and mature stages. Rice leaf photos were taken with a smartphone in natural outdoor lighting. Color calibration with Spydercheckr was needed to adjust for lighting conditions. Uncalibrated and calibrated image data were analyzed to determine RGB values converted into CIELAB color space. The L*, a*, and b* values had a significant correlation with SPAD parameters, nitrogen concentration, chlorophyll a, b, and total chlorophyll content. This connection was higher after image calibration. The study found that smartphone images could predict SPAD values with 87.9% to 92.3% precision, depending on color space. Using a smartphone digital picture of L* and a* values, N content could be estimated with 84.7% and 81.9% accuracy. Average accuracy for chlorophyll a, b, and total chlorophyll content was 65% to 76%. This study shows smartphone images can estimate rice leaf SPAD and nitrogen content.

A circuit motif for color in the human foveal retina

The neural pathways that start human color vision begin in the complex synaptic network of the foveal retina where signals originating in long (L), middle (M), and short (S) wavelength-sensitive cone photoreceptor types are compared through antagonistic interactions, referred to as opponency. In nonhuman primates, two cone opponent pathways are well established: an L vs. M cone circuit linked to the midget ganglion cell type, often called the red-green pathway, and an S vs. L + M cone circuit linked to the small bistratified ganglion cell type, often called the blue-yellow pathway. These pathways have been taken to correspond in human vision to cardinal directions in a trichromatic color space, providing the parallel inputs to higher-level color processing. Yet linking cone opponency in the nonhuman primate retina to color mechanisms in human vision has proven particularly difficult. Here, we apply connectomic reconstruction to the human foveal retina to trace parallel excitatory synaptic outputs from the S-ON (or "blue-cone") bipolar cell to the small bistratified cell and two additional ganglion cell types: a large bistratified ganglion cell and a subpopulation of ON-midget ganglion cells, whose synaptic connections suggest a significant and unique role in color vision. These two ganglion cell types are postsynaptic to both S-ON and L vs. M opponent midget bipolar cells and thus define excitatory pathways in the foveal retina that merge the cardinal red-green and blue-yellow circuits, with the potential for trichromatic cone opponency at the first stage of human vision.

ITRE: Low-light image enhancement based on illumination transmission ratio estimation

Noise, artifacts, and over-exposure are substantial challenges in the field of low-light image enhancement. Existing methods often struggle to address these issues simultaneously. In this paper, we propose a method that is based on Illumination Transmission Ratio Estimation (ITRE) to handle the challenges at the same time. Specifically, we assume that there must exist a pixel which is least disturbed by low light for pixels of each color cluster. First, we cluster the pixels on the RGB color space to find the Illumination Transmission Ratio (ITR) matrix of the whole image, which determines that noise is not over-amplified easily. Next, we consider the ITR of the image as the initial illumination transmission map to construct a base model for refining transmission map, which prevents artifacts. In addition, we design an over-exposure module that captures the fundamental characteristics of pixel over-exposure and seamlessly integrates it into the base model. Finally, there is a possibility of weak enhancement when the interclass distance of pixels with the same color is too small. To counteract this, we design an Robust-Guard (RG) module that safeguards the robustness of the image enhancement process. Extensive experiments demonstrate the superiority of the proposed method over state-of-the-art methods in terms of visual quality and quantitative metrics. Our code is available at https://github.com/wangyuro/ITRE.

Underwater image enhancement using contrast correction

AbstractLight‐induced degeneration of underwater images occurs by physical features of seawater. According to the wavelength of the colour spectrum, light reduces intensity significantly when it moves through water. The greatest wavelength of light that is visible gets absorbed first. Red and blue absorb the most and least, respectively. Because of the reducing consequences of the light spectrum, underwater images having poor contrast can be obtained. As a result, the crucial data contained inside these images will not be effectively retrieved for later analysis. The recent research suggests a novel approach to enhance the contrast while decreasing noise in underwater images. The recommended approach involves image histogram transformation using two significant colour spaces, Red‐Green‐Blue (RGB) and Hue‐Saturation‐Value (HSV). The histogram of the dominant colour channel (blue channel) in the RGB colour model is extended towards the lower level, containing a maximum limitation of 95%, while the inferior red colour channel has been extended towards the upper side, containing a minimum limitation of 5%. During the entire dynamic range, the green colour channel having the dominant and inferior colour channels expands in each direction. The Rayleigh distribution has been utilized for developing various stretching actions within the RGB colour space. The image has been converted to the HSV colour space, having the S and V elements adjusted within 1% of their minimum and maximum values. The suggested approach is examined in both qualitative and quantitative analysis. According to qualitative analysis, the recommended approach substantially boosts image contrast, lowers its blue and green effect, and minimizes over‐enhanced and under‐enhanced sections in the final resultant underwater image. The quantitative examination of 500 large scale underwater images dataset reveals that the suggested technique generates better results. The dataset images are grouped into small fish images, blue coral images, stone wall images, and coral branch images. The quantitative examination of all these four groups have been evaluated and shown. The average mean square error, peak signal to noise ratio, underwater image quality measurement, and underwater colour image quality evaluation values of dataset images are 76.69, 31.25, 3.85, and 0.64, respectively. These values of our proposed work outperform six other previous methods.

Exploring the CIEDE2000 thresholds for lightness, chroma, and hue differences in dentistry

ObjectiveTo evaluate the perceptibility and acceptability CIEDE2000 (KL:KC:KH) thresholds for lightness, chroma and hue differences in dentistry. MethodA Python-based program was developed to conduct a psychophysical experiment based on visual assessments of computer-simulated images of human teeth. The experiment was performed on a calibrated display. A 40-observer panel: dentists and laypersons (male and female; n=10), evalu- ated three subsets of simulated human teeth: the lightness subset (%∆L00 ≥ 98 %), the chroma subset (%∆C00 ≥ 98 %) and the hue subset (%∆H00 ≥ 98 %), using ∆E00 ≥ 5 units. A Takagi-Sugeno-Kang Fuzzy Approximation model was used as fitting procedure, and 50:50 % lightness, chroma and hue CIEDE2000 (1:1:1) and CIEDE2000 (2:1:1) perceptibility (PT00) and accept- ability (AT00) thresholds were calculated. Data was statistically analyzed using t-test (p < 0.05). ResultsThe 50:50 % PT00 for KL=1 were ∆L00=1.04, ∆C00=1.58 and ∆E00=1.01; and for KL=2 were ∆L00=0.51, ∆C00=1.58 and ∆E00=0.71. The 50:50 % AT00 for KL=1 were ∆L00=2.82, ∆C00=3.04 and ∆E00=2.66; and for KL=2 were ∆L00=1.40, ∆C00=3.04 and ∆E00=1.78. PT00 and AT00 ∆H00 may be considered no computable. A significant difference was found between CIEDE2000(1:1:1) and CIEDE2000(2:1:1), between lightness and chroma metrics, and between observer groups. No differences for lightness and chroma PT00 were found between male and female groups. ConclusionsIt is important to use PT00 and AT00 for lightness, chroma and hue specific to evaluate perceptual sensitivity for color changes in the tooth color space. AT00 for lightness and chroma are influenced by the observer's experience and gender. Males and laypersons show more tolerance for changes in chroma (∆C00) and in lightness (∆L00). SignificanceHue and chroma mismatch are more difficult to be accepted in dental color space.