2021
DOI: 10.2352/issn.2169-2629.2021.29.13
|View full text |Cite
|
Sign up to set email alerts
|

The Discrete Cosine Maximum Ignorance Assumption

Abstract: The performance of colour correction algorithms are dependent on the reflectance sets used. Sometimes, when the testing reflectance set is changed the ranking of colour correction algorithms also changes. To remove dependence on dataset we can make assumptions about the set of all possible reflectances. In the Maximum Ignorance with Positivity (MIP) assumption we assume that all reflectances with per wavelength values between 0 and 1 are equally likely. A weakness in the MIP is that it fails to take into acco… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
4
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
2

Relationship

0
2

Authors

Journals

citations
Cited by 2 publications
(5 citation statements)
references
References 10 publications
(14 reference statements)
0
4
0
Order By: Relevance
“…5, maximum autocorrelation is seen along the main diagonal and minimum autocorrelation is seen everywhere else. In fact, this corresponds to the so-called Maximum Ignorance with Positivity assumption [2] that was made in connection with camera characterisation (colour correction), where the vectors are all possible spectral colour signals with positive power.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…5, maximum autocorrelation is seen along the main diagonal and minimum autocorrelation is seen everywhere else. In fact, this corresponds to the so-called Maximum Ignorance with Positivity assumption [2] that was made in connection with camera characterisation (colour correction), where the vectors are all possible spectral colour signals with positive power.…”
Section: Discussionmentioning
confidence: 99%
“…The autocorrelation matrix describes the correlation between all pairs of elements of a vector or vectors that represent the same stimuli. In the domain of imaging, it is widely used to describe the spectral statistics of surface reflectance and illumination spectra [1] and is central to colour characterisation and calibration [2,3,4]. Autocorrelation matrices are also used in the context of lightness perception algorithms [5], where the vectors that we autocorrelate describe paths of pixel values through images [6].…”
Section: Introductionmentioning
confidence: 99%
“…One way to address the variation in the RGB-to-XYZ matrix is via the WPP constraint [ 12 , 13 ], which normalizes the RGB and XYZ data into the range [0, 1] using the reflectance closest to a perfect white diffuser. Effectively, all RAW RGB and CIE XYZ values are divided by the RGB and XYZ coordinates corresponding to a white patch on a color reference target.…”
Section: Methodsmentioning
confidence: 99%
“…in which tonal range refers to the image's potential different tones, a number defined by the bit depth of the image (e.g., a 12-bit RAW image maximally contains 2 12 -1 or 4095 tonal levels).…”
Section: Three Model Datasetsmentioning
confidence: 99%
See 1 more Smart Citation