Using metrics to assess the ICC perceptual rendering intent

Falkenstern, Kristyn; Bonnier, Nicolas; Pedersen, Marius; Brettel, Hans; Viénot, Françoise

doi:10.1117/12.872978

Cited by 3 publications

(7 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In an effort to provide an interpretation for the variations in the results, the test scenes characteristics were investigated. Scene busyness has been identified to correlate with image sharpness in a number of studies [16,17]. The busyness of the test images was evaluated using segmentation techniques suggested by Triantaphillidou et al [16] and by Gupta et al [18], but no strong correlations were found for the scenes outside of the average JND range.…”

Section: Scene Interpretationmentioning

confidence: 99%

Evaluation of perceived image sharpness with changes in the displayed image size

et al. 2012

View full text Add to dashboard Cite

In this paper an evaluation of the degree of change in the perceived image sharpness with changes in displayed image size was carried out. This was achieved by collecting data from three psychophysical investigations that used techniques to match the perceived sharpness of displayed images of three different sizes. The paper first describes a method employed to create a series of frequency domain filters for sharpening and blurring. The filters were designed to achieve one just-noticeable-difference (JND) in quality between images viewed from a certain distance and having a certain displayed image size (and thus angle of subtense). During psychophysical experiments, the filtered images were used as a test series for sharpness matching. For the capture of test-images, a digital SLR camera with a quality zoom lens was used for recording natural scenes with varying scene content, under various illumination conditions. For the psychophysical investigation, a total of sixty-four original test-images were selected and resized, using bi-cubic interpolation, to three different image sizes, representing typical displayed sizes. Results showed that the degree of change in sharpness between images of different sizes varied with scene content.

show abstract

Section: Scene Interpretationmentioning

confidence: 99%

Evaluation of perceived image sharpness with changes in the displayed image size

et al. 2012

View full text Add to dashboard Cite

show abstract

“…We use eight quality attributes, which are Colorimetric Accuracy (CA), Colorfulness (CO), Gamut Boundary (GB), Smoothness (SM), Details (DE), Shadows (SH), Highlights (HL), and Neutrals (NT), respectively [7]. We denote the set of all quality attributes Q.…”

Section: Cues From Numeric Pixel Valuesmentioning

confidence: 99%

“…This requirement excludes the use of time-consuming psychophysical data as the performance input. Instead, we use a set of performance results derived from metric tests, where each metric compares the color workflow performance of a specific perceptible quality attribute [6].…”

Section: Introductionmentioning

confidence: 99%

“…The psychophysical validation results used to test the quality of our first implementation showed that the observers' preference between color workflows was more significant between rendering intents than between ICC profiles. For most test images, we were able to adaptively select the same rendering intent as the observers when the observers' preference between rendering intents was significant [7]. Our goal is to improve the automated selection of which rendering intent is optimal for documents that embed several conflicting image characteristics by using semantic information.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Semantic-Driven Selection of Printer Color Rendering Intents

Falkenstern¹,

Lindner²,

Süsstrunk³

et al. 2012

cic

Self Cite

View full text Add to dashboard Cite

In this paper we introduce a unified framework that automatically selects the optimal color rendering intent for a given print job. We first present how we extract information from both the image features and the semantic information contained in keywords attached to this image. Then we show how our framework unifies the two inputs to select the optimal ICC rendering intent.The framework is evaluated with a psychophysical experiment on an image data set printed with the ICC media-relative colorimetric and perceptual intents using an Océ large format printer. We find that our method is correctly able to predict the observers preferences in 81% of the images tested when the keyword is included compared to 58% when the keyword is not included.

show abstract

“…In particular for printing applications they combine highly-nonlinear color gamut mapping and separation algorithms allowing an extremely fast evaluation of the joint transformation. As one of the key-factors affecting print quality, CLUTs were the topic of a large body of research in the past decades [2], [3], [4], [5], [6], to name a few.…”

Section: Introductionmentioning

confidence: 99%

Perceptually Optimizing Color Look-up Tables

Reinhard

Urban

2023

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

The quality of ICC profiles with embedded look-up tables (LUTs) depends on multiple factors: 1. the accuracy of the optical printer model, 2. the exploitation of the available gamut combined with the quality of the gamut mapping approach encoded in the B2A-LUTs (backwards LUTs) and 3. the tonal smoothness as well color accuracy of the backwards LUTs. It can be shown that optimizing the smoothness of the LUTs comes at the expense of color accuracy and requires gamut reduction because of internal tonal edges. We present a method to optimize backwards LUTs of existing ICC profiles w.r.t accuracy, smoothness, gamut exploitation and mapping, which can be extended beyond color, e.g. to joint color and translucency backward LUTs. The approach is based on a perceptual difference metric that is used to optimize the LUT's tonal smoothness constrained to preserve both the accuracy of and the relationship between colors.

show abstract

Using metrics to assess the ICC perceptual rendering intent

Cited by 3 publications

References 16 publications

Evaluation of perceived image sharpness with changes in the displayed image size

Evaluation of perceived image sharpness with changes in the displayed image size

Semantic-Driven Selection of Printer Color Rendering Intents

Perceptually Optimizing Color Look-up Tables

Contact Info

Product

Resources

About