Content-aware contrast ratio measure for images

Ortiz-Jaramillo, B.; Kumcu, Asli; Platiša, Ljiljana; Philips, Wilfried

doi:10.1016/j.image.2017.12.007

Cited by 11 publications

(12 citation statements)

References 22 publications

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“…Unfortunately, little research has investigated responses to image contrast (Lu et al, 2015). Most existing literature on image contrast focuses on image enhancement techniques for digital images, electronic displays, or printers (e.g., Fidler et al, 2007;Morrow et al, 1992;Ortiz-Jaramillo et al, 2018;Panetta et al, 2013;Shiao et al, 2007). However, one study in which subjects rated the pleasantness of printed image reproductions found that photographs with enhanced image contrast were rated as more pleasing (Pedersen et al, 2010).…”

Section: Consumer Liking and Contrastmentioning

confidence: 99%

“…Peak luminance and average luminance information are readily available from histograms of the luminosity or brightness intensity on professional digital cameras, image editing software (e.g., Photoshop), and the scientific image-processing program ImageJ (Schneider et al, 2012). The advantage of histogram-based physical measures is that the much greater consistency across applications than statistical measures or local contrast measures (Morrow et al, 1992;Panetta et al, 2013) and their high correlation with viewers' perceptions of image contrast (Ortiz-Jaramillo et al, 2018;Pedersen et al, 2010). Therefore, to supplement image contrast analysis via image file size, the research team generated luminosity histograms for each of the images in the sample using ImageJ software, and computed the physical contrast measure using King-Smith and Kulikowski's (1975) formula.…”

Section: Image Contrast Measuresmentioning

confidence: 99%

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Perfect social media image posts: symmetry and contrast influence consumer response

Kostyk

Huhmann

2021

EJM

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Purpose Two studies investigate how different structural properties of images – symmetry (vertical and horizontal) and image contrast – affect social media marketing outcomes of consumer liking and engagement. Design/methodology/approach In Study 1’s experiment, 361 participants responded to social media marketing images that varied in vertical or horizontal symmetry and level of image contrast. Study 2 analyzes field data on 610 Instagram posts. Findings Study 1 demonstrates that vertical or horizontal symmetry and high image contrast increase consumer liking of social media marketing images, and that processing fluency and aesthetic response mediate these relationships. Study 2 reveals that symmetry and high image contrast improve consumer engagement on social media (number of “likes” and comments). Research limitations/implications These studies extend theory regarding processing fluency’s and aesthetic response’s roles in consumer outcomes within social media marketing. Image posts’ structural properties affect processing fluency and aesthetic response without altering brand information or advertising content. Practical implications Because consumer liking of marketing communications (e.g. social media posts) predicts persuasion and sales, results should help marketers design more effective posts and achieve brand-building and behavioral objectives. Based on the results, marketers are urged to consider the processing fluency and aesthetic response associated with any image developed for social media marketing. Originality/value Addressing the lack of empirical investigations in the existing literature, the reported studies demonstrate that effects of symmetry and image contrast in generating liking are driven by processing fluency and aesthetic response. Additionally, these studies establish novel effects of images’ structural properties on consumer engagement with brand-based social media marketing communications.

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Section: Consumer Liking and Contrastmentioning

confidence: 99%

Section: Image Contrast Measuresmentioning

confidence: 99%

Perfect social media image posts: symmetry and contrast influence consumer response

Kostyk

Huhmann

2021

EJM

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“…Luminance values of the actual task were measured using Hagner S3 Photometer and the contrast ratios were obtained using Weber's formula (Equation ( 2)) [58] for both charts displayed in Figure 2, which were calculated using the background luminance of the task (Lb)…”

Section: Visual Tasks Properties In the Virtual Environmentsmentioning

confidence: 99%

Developing an Innovative Method for Visual Perception Evaluation in a Physical-Based Virtual Environment

Abd-Alhamid

Kent

Bennett

et al. 2019

Building and Environment

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With the developments in virtual reality technologies, significant researches have been conducted for human response on indoor luminous environment using head-mounted display to replace those in real environment. However, the limited resolution and luminance values offered by the devices might affect the perceived appearance and high-order impressions in the simulated virtual environment. In this study, a simulated 3-dimensional virtual office was compared against a real one. Both settings presented similar physical and luminous conditions to twenty participants (N=20). The study investigated subjective and objective visual responses and participants' interaction with the virtual environment based on measurements of perceived presence. Subjective assessments included questions on luminous environment appearance (brightness, colour-temperature, distribution) and high-order perceptions (pleasantness, interest, spaciousness, excitement and complexity). Objective assessments measured contrastsensitivity and colour-discrimination tasks to assess visual performance across the two representation environments. Results showed no significant differences between the two environments based on the studied parameters, indicating a high level of perceptual accuracy of appearance and high-order perceptions. Minor physical symptoms related to the headset 2 use and high level of perceived presence were found, indicating the proposed methodology's capability to provide realistic immersive environments. Although attributes regarding scene quality: colours, details, and contrast were perceived significantly different to the real environment, objective tasks showed that similar contrast and colour appearance can be produced in the virtual environment with minor impact on fine-details due to limited resolution.Virtual reality maybe a promising alternative representation medium to investigate visual perceptions as the overall appearance of the scene can still be correctly acquired.

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“…values [78,79,80]. Therefore, our test data is composed of 25 source images and their corresponding 375 distorted images (25 source images × 3 types of distortions × 5 levels of distortions); thus a total of 400 test images.…”

Section: Evaluation Methodsmentioning

confidence: 99%