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Contrast plays an important role in human perception of image quality. In this paper, we propose a metric for no-reference quality assessment of contrast-changed images by using a novel semi-supervised robust PCA , which can realize feature selection and denoising simultaneously, guided by the available supervisory information. To select features adaptively, the information-oriented features (e.g. entropy and natural scene statistics) and appearance-oriented features (e.g. colorfulness) are adopted. The proposed model is formulated as a constraint optimization problem , which is further casted to a convex problem and solved via augmented Lagrangian multiplier method. Extensive experimental results on CCID2014, CSIQ, SIQAD and TID2013 databases show that the proposed semi-supervised image quality metric based on robust PCA (SIQMR) provides a more accurate prediction than other metrics on the human perception of contrast variations.

With the development of underwater image processing, more and more underwater image restoration algorithms have been proposed. To date, most underwater image degradation perception methods are ineffective in multi-color environments. In this paper, we proposed a referenceless image degradation perception method based on the underwater imaging model. Our method includes the colorfulness index, contrast index, and sharpness index. The colorfulness index is used to measure the color loss. The contrast index and sharpness index are used to measure the blurring. For the contrast index and sharpness index, we proposed a grayscale conversion method that can adaptively adjust the coefficients of red, green, and blue (RGB) values. Their generalization ability under multi-colored environments can be enhanced by the proposed adaptive grayscale conversion method. It is useful for underwater images that are normally dominated by green and blue color channels. Compared with the leading evaluation metrics available in the literature, our proposed method can better perceive the degradation of underwater images. No matter under what lighting conditions, our proposed three indexes can decrease with the degradation of images. It makes our proposed three indexes can be effective in both general underwater scenarios and hash scenarios. The correlation coefficients of our proposed indexes are the largest. More importantly, the proposed indexes can be used to process underwater images in real time and evaluate the performance of underwater image restoration algorithms. Our proposed indexes can also be applied to monitor the turbidity of water and improve the underwater image restoration algorithms. A single proposed index cannot comprehensively perceive the quality of underwater images. The proposed three indexes need to be integrated into an evaluation metric in future work.

We have developed a computational model and a test model for predicting the brightness, a visual perception term, achieved by high-dynamic-range (HDR) televisions and computer monitors, in particular, those with materials that feature primary colors having high spectral purity. Quantum dot materials in combination with emissive pixel lighting is expanding the color gamut level to attain a degree of performance unmatched in today’s consumer and professional displays. These displays exhibit highly significant improvements in colorfulness and brightness owing to their narrowband primaries and to the Helmholtz–Kohlrausch (H–K) effect, where colors—notably reds and blues—are perceived as far brighter than their reference white. We blended the iCAM06 computational model originally designed for HDR image evaluation with an improved treatment of vivid colors. Our data collection relied on a rare, yet commercially available 2D spectroradiometer that captures up to 1.4 million samples of spectral data across any image, an achievement impossible to attain manually. Our approach coupled nicely with the filter image-processing steps in iCAM06. Finally, we extracted image brightness for white, red, and blue for determining the SEMI perceptual contrast length (PCL) to assess the predicted improvement level of our model and then matched those results to subjective experiments. As a result, we have a methodology that can be used by display makers to measure and predict brightness as a function of colorfulness to optimize luminance—notably for reducing power—in displays with spectrally pure primaries .

The article highlights the environmental education of Primary School students on the basis of an integrated approach,their contribution to the comprehensive formation of environmental etiquette, the Prevention of pollution of water, soil, air, the preservation of the colorfulness of plants and animals, the negative consequences of the actions committed to nature.

This study quantitatively analyzed how the color appearance of textile fabrics of varying lightness, chroma, and hue changes under a broad spectrum of illumination conditions. The color appearances of 203 chromatic (R, YR, Y, GY, G, BG, B, and RB) and achromatic fabrics were measured spectroradiometrically under 16 light sources with different correlated color temperatures (2700, 4000, 5000, and 6500 K) and luminances (50, 100, 500, and 1000 cd/m2). The color appearance values were compared with the spectrophotometrically measured physical color values of the fabrics, which were regarded as their true colors. The differences ranged up to 12.45 ∣ΔL*10∣, 19.32 ∣ΔC*ab,10∣, 161.83∣Δhab, 10∣, and 16.70 ΔECMC(2:1) units, which were significantly higher than the color discrimination thresholds of the human eye. Among 9 hue groups of fabrics, the color appearances of BG (bluish-green) fabrics were generally most affected by light sources, while those of achromatic fabrics were least affected with mean ΔECMC(2:1) of 6.32 and 1.03, respectively. It was also found that the correlated color temperature of light sources significantly affected all the lightness, colorfulness, and hue of fabrics, and the different trends of these effects were observed for different hue groups. Unlike current knowledge, light sources with a correlated color temperature of 6500 K did not always produce color appearances that most closely resemble the actual physical colors. Meanwhile, the luminance of light sources did not have a profound effect on the color appearances of fabrics.

Currently, polarization visualization strategies are accomplished by mapping polarization information into a perceptually uniform color appearance model CAM02-UCS. However, the deviation of the CAM02-UCS space from the lightness prediction results in an inaccurate match between the polarization information and the perceptual information. In this paper, we propose a novel polarization visualization strategy based on the perceptual uniform space J z a z b z . The polarization visualization be completed by placing the polarization information into the lightness J z , colorfulness C z and hue angle h z channels of the J z a z b z space. The experimental results show that the proposed method can significantly improve the lightness of the low irradiance and high polarization region, hence more polarization information can be sensed by human visual system.

We previously reported useful methods that can be implemented to identify intersegmental boundary lines (IBLs) by using an intravenous indocyanine green (ICG) fluorescence imaging system (ICG-FS) during a thoracoscopic anatomical segmentectomy (TAS). The aim of this study was to evaluate the recently released third-generation ICG-FS that features an emphasizing xenon-light source for IBL identification.We prospectively studied cases involving 106 consecutive patients who underwent TAS. Intraoperatively, we used the third-generation ICG-FS, the conventional ICG methods (CIM) emphasizing xenon-light (CIM-X), and the spectra-A method (SAM) emphasizing xenon-light (SAM-X), for IBL identification. Furthermore, 16 of the 106 patients (15%) could be simultaneously evaluated using old-generation ICG-FSs, CIM, and SAM. All images were completely quantified for illuminance and for three colors, red, green, and blue.IBLs were successfully identified in all the patients (100%) with no adverse events. The SAM-X significantly increased the illuminance, especially in the resecting segments, compared to the CIM (39.0 versus 22.2, P 0.01) and SAM (39.0 versus 29.3, P 0.01), with enhanced red color compared to the CIM (33.1 versus 21.9, P 0.01) and SAM (33.1 versus 14.0, P 0.01). Furthermore, the SAM-X significantly increased the illuminance contrast compared to the CIM-X (34.1 versus 15.3, P 0.01).The present study suggests that the SAM-X potentially provided images with the highest visibility and colorfulness compared to the older generation ICG-FSs or CIM-X. Secure IBL identification can be more easily and safely performed using the SAM-X.

Tone mapping operators (TMOs) are developed to convert a high dynamic range (HDR) image into a low dynamic range (LDR) one for display with the goal of preserving as much visual information as possible. However, image quality degradation is inevitable due to the dynamic range compression during the tone-mapping process. This accordingly raises an urgent demand for effective quality evaluation methods to select a high-quality tone-mapped image (TMI) from a set of candidates generated by distinct TMOs or the same TMO with different parameter settings. A key element to the success of TMI quality evaluation is to extract effective features that are highly consistent with human perception. Towards this end, this paper proposes a novel blind TMI quality metric by exploiting both local degradation characteristics and global statistical properties for feature extraction. Several image attributes including texture, structure, colorfulness and naturalness are considered either locally or globally. The extracted local and global features are aggregated into an overall quality via regression. Experimental results on two benchmark databases demonstrate the superiority of the proposed metric over both the state-of-the-art blind quality models designed for synthetically distorted images (SDIs) and the blind quality models specifically developed for TMIs.

Images captured in low-light conditions often suffer from bad visibility, e.g., low contrast, lost details, and color distortion, and image enhancement methods can be used to improve the image quality. Previous methods have generally obtained a smooth illumination map to enhance the image but have ignored details, leading to inaccurate illumination estimations. To solve this problem, we propose a multi-scale attention retinex network (MARN) for low-light image enhancement, which learns an image-to-illumination mapping to obtain a detailed inverse illumination map inspired by retinex theory. In order to introduce more image priors, we introduce a novel illuminance-attention map to guide the model to characterize varying-lighting areas, which we combine with the low-light image as the model input. MARN consists of a multi-scale attention module and a feature fusion module; the former extracts multi-resolution features with attention-based feature aggregation, while the latter further merges the output features of the previous module with the input. To achieve better visibility, we formulate a novel loss function to synthetically measure the illumination, detail, and colorfulness of the image. Extensive experiments are performed on several benchmark datasets. The results demonstrate that our method outperforms other state-of-the-art methods according to both objective and subjective metrics.

A tone-mapped image (TMI) obtained from the corresponding high dynamic range (HDR) image induces artifacts and distortion, which might result in the loss of structure information and impaired color. By analyzing the visual characteristics of TMIs, this work proposes a robust blind visual quality evaluation method for TMIs by using gradient and chromatic statistics (VQGC). First, motivated by the perceptual mechanism that the human visual system (HVS) is sensitive to image structure variation, we employ the gradient features to measure structure degradation in TMIs. To predict structure distortion accurately, we compute the gradient magnitude and orientation to measure image structure variation, and the relative gradient magnitude and orientation are also computed to capture microstructure change. Second, the color invariance descriptors are utilized to capture the visual degradation of colorfulness by local binary pattern (LBP) on four chromatic feature maps. Finally, the gradient and chromatic features are combined together as the final quality-aware feature vector, which is applied to assess the perceptual quality of TMIs by support vector regression (SVR). Comparison experiments show that the performance of the proposed method is better than other existing blind quality assessment methods on public databases.

Purpose The success of e-commerce websites depends on their effective communication and influence on their users. At first glance, the users are impressed by the website design and, if inspired, they would continue their operations on the website. This paper aims to evaluate the effects of visual aesthetic of the Web pages on users’ behavior in online shopping environment. In particular, the paper aims to evaluate the elements of visual aesthetic on the organism variables (i.e. “satisfaction,” “arousal,” “perceived on-line service quality” and “trust”) and measure them on the users’ response (i.e. purchase, comparison and re-visit). Design/methodology/approach Using the stimulus–organism–response (S-O-R) framework, the authors first assessed direct and indirect effects of visual aesthetics of e-commerce websites on customer responses. Then, the Visual Aesthetics of Websites Inventory (VisAWI) method was used to examine the effects of four dimensions (i.e. craftsmanship, simplicity, diversity and colorfulness) on users’ perceived website aesthetics. To do so, DigiKala.com, a famous Iranian e-commerce website was selected and the questionnaires were distributed among its users. Findings The study results revealed that the website aesthetics in the S-O-R evaluation had the greatest direct impact on “perceived quality of online services,” “trust,” “satisfaction” and “arousal,” respectively. These variables also indirectly affected “shopping,” “revisit” and “comparison to similar products on other websites.” Regarding the evaluations based on the VisAWI, the component “craftsmanship” played the most central role in expressing the website aesthetics, followed by the variables “simplicity,” “diversity” and “colorfulness,” respectively. Originality/value Although the considerable effect of Web aesthetics on customers’ purchase behavior has been identified in previous research, it has not been accurately measured. Furthermore, studies on Web aesthetics are mostly limited to information systems’ users and do not concern consumers. Therefore, considering the increasing growth in online shopping and the significance of Web aesthetics to online consumers, investigating how consumers respond to Web aesthetics is of vital importance.

Tone mapping operators (TMOs) reproduce the high dynamic range (HDR) images on low dynamic range (LDR) consumer electronics devices such as monitors or printers. To accurately measure and compare the performance of different TMOs, this article proposes a tone-mapped images (TMIs) luminance partition model and corresponding quality measure. First, each tone-mapped (TM) image is segmented into highlight region (HR), dark region (DR) and midtone region (MR) based on luminance partition. Second, local entropies and contrast features are extracted in the HR and DR, and color-based features are captured in the MR. Meanwhile, the gray-level co-occurrence matrix (GLCM) and Canny operator are utilized to measure the microstructural distortions and halo effects, respectively. Finally, all extracted features are combined and trained together with subjective ratings to form a regression model using support vector regression (SVR). Experimental results show that the proposed method outperforms the state-of-the-art no-reference (NR) methods. Specifically, the spearman correlation coefficients (SRCC) values of our method reach 0.83 and 0.76 on the tone-mapped image database (TMID) and the ESPL-LIVE HDR database, respectively.

A new restoration model for underwater images is presented, based on the dark channel reflection-illumination decomposition and local backscattering lighting estimation, to promote the clarity of edge detail and the colorfulness of the output image. For turbid offshore environments, a novel underwater image restoration method is further introduced by applying a statistical prior to the offshore attenuation coefficients. It is shown that the estimated transmission map with lighting-reflection decomposition, rather than dividing the dark channel by the maximal backscattering light as the other methods do, provides better clarity and color restoration. Detailed qualitative and quantitative analysis on hundreds of underwater images is performed, which demonstrates that the proposed method outperforms the state-of-the-art algorithms on the images taken in offshore water characterized by a heavy concentration of colored dissolved organic matter and total suspended matter, and is suitable for fast underwater processing.

Assessing the perceptions of urban parks and understanding the relationships between environmental features and park perceptions are critical to the design and management of urban parks. However, it is challenging to quantify park perceptions at a large spatial-temporal scale. In addition, little is known about which environmental features contribute to different perceptions in cross-cultural comparison. This study collected 21462 and 28398 online reviews from 2015 to 2020 and applied Natural Language Processing (NLP) methods to understand how people perceive urban parks and which environmental features contribute to positive and negative park perceptions in Shanghai and Brussels. We quantified park perceptions through sentiment analysis and transformed unstructured high-frequency words into structured environmental feature clusters corresponding to positive and negative perceptions using the word2vec models and machine learning. In addition, we created Chinese-based and English-based environmental feature lexicons for standardized measurement of the perceived frequencies of different environmental features and their associations with park perceptions. The results of our sentiment analysis showed that urban parks were generally perceived positively in both cities. We also identified 15 environmental feature clusters that were related to positive and negative perceptions. Beta regression results showed that the perceived frequencies of water bodies and colorfulness could be used as positive predictors for park perceptions in Shanghai. In contrast, the perceived frequency of tranquility had a positive impact on park perceptions in Brussels. We found that the perceived frequency of crowdedness was also positively associated with park perceptions in Brussels, suggesting the potential use conflicts between different user groups in urban parks. This study can provide insights into park design and management from cross-cultural comparison through systematic identification and standardized assessment of landscape preferences using online reviews and NLP methods.

Automatic image quality assessment similar to human vision perception is an essential process for real-time image processing applications to perform perceptual image assessments for effectively achieving their goals. As no-reference image quality assessment (NR-IQA) schemes perform perceptual assessments of images without any information about their original version, these algorithms suit real-time computer vision techniques because of the non-availability of reference images. Contrast and colorfulness play important roles in determining the quality of color images. By combining many IQA metrics, a number of combined metrics had been devised. This study provides an insight into major NR-IQA methods and their effectiveness in assessing contrast, colorfulness, and overall quality of contrast-degraded images with technical analysis. The effectiveness of top-ranking NR-IQA methods is experimentally assessed with benchmark assessment methods on images from benchmarked databases. The study provides insight into open research challenges in the area of NR-IQA for developing new promising methods by clearly demarcating the difficulties of top-ranking NR-IQA methods.

Accurately predicting the quality of depth-image-based-rendering (DIBR) synthesized images is of great significance in promoting DIBR techniques. Recently, many DIBR-synthesized image quality assessment (IQA) algorithms have been proposed to quantify the distortion that existed in texture images. However, these methods ignore the damage of DIBR algorithms on the depth structure of DIBR-synthesized images and thus fail to accurately evaluate the visual quality of DIBR-synthesized images. To this end, this paper presents a DIBR-synthesized image quality assessment metric with Texture and Depth Information, dubbed as TDI. TDI predicts the quality of DIBR-synthesized images by jointly measuring the synthesized image's colorfulness, texture structure, and depth structure. The design principle of our TDI includes two points: (1) DIBR technologies bring color deviation to DIBR-synthesized images, and so measuring colorfulness can effectively predict the quality of DIBR-synthesized images. (2) In the hole-filling process, DIBR technologies introduce the local geometric distortion, which destroys the texture structure of DIBR-synthesized images and affects the relationship between the foreground and background of DIBR-synthesized images. Thus, we can accurately evaluate DIBR-synthesized image quality through a joint representation of texture and depth structures. Experiments show that our TDI outperforms the competing state-of-the-art algorithms in predicting the visual quality of DIBR-synthesized images.

Background and Objectives:Prior research has successfully identified linguistic and behavioral patterns associated with schizophrenia spectrum disorders (SSD) from user generated social media activity. Few studies, however, have explored the potential for image analysis to inform psychiatric care for individuals with SSD. Given the popularity of image-based platforms, such as Instagram, investigating user generated image data could further strengthen associations between social media activity and behavioral health.Methods:We collected 11,947 Instagram posts across 68 participants (mean age = 23.6; 59% male) with schizophrenia spectrum disorders (SSD;n= 34) and healthy volunteers (HV;n= 34). We extracted image features including color composition, aspect ratio, and number of faces depicted. Additionally, we considered social connections and behavioral features. We explored differences in usage patterns between SSD and HV participants.Results:Individuals with SSD posted images with lower saturation (p= 0.033) and lower colorfulness (p= 0.005) compared to HVs, as well as images showing fewer faces on average (SSD= 1.5,HV= 2.4,p< 0.001). Further, individuals with SSD demonstrated a lower ratio of followers to following compared to HV participants (p= 0.025).Conclusion:Differences in uploaded images and user activity on Instagram were identified in individuals with SSD. These differences highlight potential digital biomarkers of SSD from Instagram data.

The article shows the results of research of the mutual influence of the melodic element and the harmonic side in Sergei Rachmaninoff”s musical compositions. The object of the study is presented by the linear functions of harmony, the elements of the harmonic vertical in the melodic line, and the processes of the melodic enrichment of the chords and the harmonic progressions. In a whole set of cases the chord or chord progression become the objects of melodic development through the saturation by means of non-chordal pitches and entire intervallic-chordal strata, through melodic development in separate textural lines. Simultaneously there occurs the saturation of the melody with all the richness of harmonic colors, which becomes possible because of the tight interaction between all the textural strata. Especially important for the issue of the correlation between melodicism and the harmonic processes in the musical texture in general is the problem of phonism. The conclusion is arrived at that all the levels of harmony demonstrate the activity of general tendencies: modal transformations of the harmonic interpretation of melodic units (the reharmonization of the tone, motive, syntagm and theme); the avoidance of traditional progressions; the overcoming of insularity and univocity of harmonic interpretation of the melodic unit; the prevalence of tension and procedurality; attention to the phonic side. The melodic and harmonic motion interacts with each other in the tightest way, and this not only does not impoverish the melody, but endows it with additional colorfulness and capaciousness, at the same time endowing the harmony with special intonational satiety. Keywords: Sergei Rachmaninoff, Rachmaninoff’s melodicism, Rachmaninoff’s harmony, the interaction between the melodic element and harmony.

The aim of the study was to investigate the impact of color and color combinations on visual attractiveness in food. Ten salad mixes were composed to represent pale-colored salads with no color contrasts and colorful salads with high color contrasts. A consumer study was conducted to discover the kind of color combinations that are seen as visually attractive in salad mixes. The salad portions were photographed and the photographs were introduced in the consumer study (n = 93). In addition, the color values (L*, a*, b*) of the salad compounds were measured using digital image analysis. The color dimensions chroma, saturation, vividness, depth and hue angle were calculated. The total color differences and the maximum differences between the salad components’ color values and color dimensions were calculated to describe the color contrasts in the salad portions. The results of the study indicated that colors and color combinations impacted visual attractiveness and that colorful salad portions with high color contrasts between salad components were more attractive than pale-colored salads with no color contrasts. Saturation and depth of color were the color dimensions associated with attractiveness. Color contrasts with the complementary colors red and green and with light and dark colors were preferred, along with high total color differences. The colorfulness and the color contrasts were associated with freshness, variability and complexity, and consequently with attractiveness. The improvement of visual attractiveness in food using intensive colors and stimulating color combinations is one potential way to tempt consumers to choose and consume more vegetables.

Research on how color affects psychological functioning has grown in recent years. The majority of these studies have focused on hue or lightness, while paying little attention to chroma. The present research sought to address this oversight in the literature by investigating the degree to which chroma influences person perception, specifically with regard to the Big Five personality traits. Drawing from Conceptual Metaphor Theory, we predicted that perceiving literal colorfulness (i.e., chroma) would influence perceptions of figurative colorfulness (i.e., extraversion, openness). In Experiment 1, participants (n = 351) rated the Big Five personality traits of a male target wearing a high or low-chroma blue shirt. In Experiment 2, participants (n = 164) rated personality traits of 10 male targets surrounded by high versus low-chroma colored borders (green, blue, and red). In Experiment 3, participants (n = 203) rated personality traits of 18 (male and female) targets wearing high and low-chroma clothing (green, blue, and red). Targets surrounded by (or wearing) high-chroma colors were perceived as more extraverted and open than when surrounded by (or wearing) low-chroma colors. A meta-analytic summary indicates a robust effect. Chroma is a perceptual variable that can influence first impressions of personality.

This study aims to explore the relationships between Instagram user characteristics and color features of their photos. Based on the assumption that individuals who are similar in characteristics would exhibit a similar style in their social media photos, this study pays attention to color as one of the key elements of style. An online survey to 179 university students measured their Big Five personality traits, narcissism, life satisfaction, loneliness, attitude to Instagram, and gender, and their Instagram photos were analyzed in terms of colorfulness, color diversity, and color harmony. Total 25,394 photos were analyzed. Results suggest that (1) agreeableness was the most relevant user variable associated with all color features; (2) the color features of Instagram photos were different by gender; (3) neuroticism of Instagram users was negatively associated with the color harmony of their photos; (4) color diversity was negatively correlated with loneliness, and romantic loneliness in particular; (5) users’ attitude to Instagram and the color harmony of their photos were positively correlated; and (6) extraversion of Instagram users was positively correlated with the color diversity while openness was negatively correlated with the color diversity and color harmony of their photos. They reveal that the color features of Instagram photos are linked to the characteristics of their uploader. The present study contributes to the body of research on color preference and online self-presentation. It not only exemplifies how the stylometric approach can be adopted to SNS research but also presents the link between user characteristics and the features of their posts at pixel-level, including color.

Tone-mapping operators (TMOs), which are designed to convert high dynamic range (HDR) images to standard low dynamic range (LDR) images for displaying on conventional devices, have gained extensive attention recently. The quality of tone-mapped images generated by different TMOs varies significantly, which depend upon the image contents and the parameter settings. A quality index that can accurately evaluate the performances of TMOs is thus highly needed. With this motivation, this paper presents a blind quality index based on luminance partition for tone-mapped images. It is based on the fact that the Human Visual System (HVS) has different sensitivities to image regions with different luminance levels. Specifically, two adaptive thresholds are first employed to segment an image into the dark, bright and normal areas. Then, we calculate the quality-aware features from different luminance areas: 1) local entropy feature is extracted from the dark and bright areas to measure the information loss due to the overexposure or underexposure during the tone mapping process; 2) local colorfulness feature is extracted from the normal area to evaluate the reproduction of colors. With the consideration that the perception of image quality depends on the combined effects of the salient local distortion and global quality degradation, the global contrast feature is also calculated and integrated for better evaluation performance. Moreover, to take advantage of the hierarchical characteristic of the HVS, all features are calculated under a multi-resolution framework. Eventually, the extracted features are mapped into an objective quality score based on the random forest regression. The proposed metric is shown to outperform those state-of-the-art metrics according to extensive experiments conducted on two publicly available databases.

Nowadays, high-dynamic-range (HDR) imaging represents a prevailing trend and attracts much attention from both academic and industrial scholars. Since HDR images cannot be properly produced on the mainstream low-dynamic-range (LDR) displays, various tone-mapping operators or postprocessing technologies have been designed to transform HDR images into LDR images for visualization on LDR displays. However, it inevitably induces artifacts and distortions due to dynamic range compression. Besides, existing tone-mapped (TM) technologies cannot effectively handle all kinds of images with diverse contents and structures, leaving to a very challenging and urgent image quality assessment (IQA) problem. To cope with this challenge, in this paper, an effective blind quality assessment approach for TM images is proposed through a comprehensive consideration of their characteristics. More specifically, to dig out sufficient information from TM images, multiple quality-sensitive features are captured to fully represent different attributes, including colorfulness, naturalness, and structure. The connection between feature space and associated subjective ratings is established via a regression model. Extensive experiments on a recently released TM image database prove that the proposed approach is superior to the state-of-the-art no-reference IQA approaches.

As web has become the most popular media to attract users and customers worldwide, webpage aesthetics plays an increasingly important role for engaging users online and impacting their user experience. We present a novel method using deep learning to automatically compute and quantify webpage aesthetics. Our deep neural network, named as Webthetics, which is trained from the collected user rating data, can extract representative features from raw webpages and quantify their aesthetics. To improve the model performance, we propose to transfer the knowledge from image style recognition task into our network. We have validated that our method significantly outperforms previous method using hand-crafted features such as colorfulness and complexity. These promising results indicate that our method can serve as an effective and efficient means for providing objective aesthetics evaluation during the design process.

This study focuses specifically on content-based image retrieval. The objective being to characterize the images according to their content, in the form of an index, a score which will be used for the manipulation of the images.We propose two visual perception quantification methods, one for colorfulness and one for depth. In terms of quantifying an impression, it was necessary to set up objective and rigorous criteria reflecting a sometimes subjective concept.Based on saturation, lightness and color disparity, we propose a framework that assigns to image an index between 0 and 100 to convey the impression that the image is very colorful or not. While in the case of depth, the estimate of a depth map will serve as a starting point. We estimate that the impression of depth is linked to the spread of the elements that make up the image from the farthest to the closest.

We propose a method to recover and restore art-work that has been damaged over time due to several factors. Our method produces great results by completely removing damages in most of the images and perfectly estimating the damaged region. We achieved accurate results due to (i) a custom data augmentation technique which depicts realistic damages rather just blobs (ii) novel CResNetBlocks that subsequently upsample and downsample features to restore the image with efficient backpropagation measures, and (iii) the choice of using patch-discriminators to achieve sharpness and colorfulness. Our network architecture is a conditional Generative Adversarial Network where the generator uses a combination of adversarial loss, L 1 loss and the discriminator uses binary cross-entropy loss for optimization. While the expressiveness of existing comparison methods is limited, we present our results with several metrics for future comparison and showcase some visuals of recovered artwork. PyTorch implementation is available at: https://github.connamasl91297/artgan.