Cone Response Function Research Articles

Spectral Filter Selection Based on Human Color Vision for Spectral Reflectance Recovery.

Spectral filters are an important part of a multispectral acquisition system, and the selection of suitable filters can improve the spectral recovery accuracy. In this paper, we propose an efficient human color vision-based method to recover spectral reflectance by the optimal filter selection. The original sensitivity curves of the filters are weighted using the LMS cone response function. The area enclosed by the weighted filter spectral sensitivity curves and the coordinate axis is calculated. The area is subtracted before weighting, and the three filters with the smallest reduction in the weighted area are used as the initial filters. The initial filters selected in this way are closest to the sensitivity function of the human visual system. After the three initial filters are combined with the remaining filters one by one, the filter sets are substituted into the spectral recovery model. The best filter sets under L-weighting, M-weighting, and S-weighting are selected according to the custom error score ranking. Finally, the optimal filter set is selected from the three optimal filter sets according to the custom error score ranking. The experimental results demonstrate that the proposed method outperforms existing methods in spectral and colorimetric accuracy, which also has good stability and robustness. This work will be useful for optimizing the spectral sensitivity of a multispectral acquisition system.

Using Smooth Metamers to Estimate Color Appearance Metrics for Diverse Color-Normal Observers.

Color-normal subjects sometimes disagree about metameric matches involving highly structured SPDs, because their cone fundamentals differ slightly, but non-negligibly. This has significant implications for the design of light sources and displays, so it should be estimated. We propose a broadly applicable estimation method based on a simple adaptive "front-end" interface that can be used with any selected standard color appearance model. The interface accepts, as input, any set of color matching functions for the individual subject (for example, these could be that person's cone response functions) and also the associated tristimulus values for the test stimulus and also for the reference stimulus (i.e. reference white). The interface converts this data into tristimulus values of the form used by the selected color appearance model (which could, for example, be X, Y, Z), while also carrying out the needed transform, which is based on an estimate of the subject's likely previous long-term adaptations to their unique cone fundamentals. The selected standard color appearance model then provides color appearance data that is an estimate of the color appearance of the test stimulus, for that individual subject. This information has the advantage of being interpretable within that model's well-known color space. The adaptive front end is based on the fact that, for any selected input SPD and the subject's unique color matching functions, there can be many different SPDs that are metameric for that individual. Since observer-to-observer color perception differences are minimized for spectrally smooth SPDs, smooth metamers predict color appearances reasonably accurately.

Designing a Color Filter via Optimization of Vora-Value for Making a Camera more Colorimetric

Previous work has proposed to solve for a filter which, when placed in front of a camera, improves the colorimetric property by best satisfying the Luther condition. That is, the filtered spectral sensitivities of a camera - after a linear transform - are as close to the color matching functions of the human visual system as possible. By construction, the prior art solves for a filter for a given set of human visual sensitivities, e.g.the XYZ color matching functions or the cone response functions. However, depending on the target spectral sensitivity set, a different optimal filter is found. In this paper, we set out a method to solve for a filter that works equally well for all possible target sensitivity sets of the human visual system. We observe that the cone fundamentals, the CIE XYZ color matching functions or any linear combination thereof, span the same vector space. Thus, we solve for a filter that makes the vector space spanned by the filtered camera sensitivities as similar as possible to the space spanned by human vision sensors. We argue that the Vora-Value is a suitable way to measure subspace similarity and we develop an optimization method for finding a filter that maximizes the Vora-Value measure. Experiments demonstrate that our new optimization leads to the filtered camera sensitivities which have a significantly higher Vora-Value and improved colorimetric performance compared with antecedent methods.

인간 시각 인지에 기반을 둔 높은 동적폭을 갖는 영상 보정 방법

For last several decades, the color correction methods have been proposed for HDR(high dynamic range) images. However, color distortion problems take place after correcting the colors such as halos, dominant color as well known. Accordingly, this article presents a novel approach in which the method consists of tone-mapping method and cone response function. In the proposed method, the tone mapping method is used to improve the contrast in the given HDR image based on chromatic and achromatic based on the CIEXYZ tristimulus value, expressed in c/m. The cone response function is used to deal with mismatch between corrected image and displayed image as well as to estimate various human visual effects based on the CMCAT2000 color appearance model. The experimental results show that the proposed method yields better performance of color correction over the conventional method in subjective and quantitative quality, and color reproduction.

Modeling a color-rendering operator for high dynamic range images using a cone-response function

Tone-mapping operators are the typical algorithms designed to produce visibility and the overall impression of brightness, contrast, and color of high dynamic range (HDR) images on low dynamic range (LDR) display devices. Although several new tone-mapping operators have been proposed in recent years, the results of these operators have not matched those of the psychophysical experiments based on the human visual system. A color-rendering model that is a combination of tone-mapping and cone-response functions using an XYZ tristimulus color space is presented. In the proposed method, the tone-mapping operator produces visibility and the overall impression of brightness, contrast, and color in HDR images when mapped onto relatively LDR devices. The tone-mapping resultant image is obtained using chromatic and achromatic colors to avoid well-known color distortions shown in the conventional methods. The resulting image is then processed with a cone-response function wherein emphasis is placed on human visual perception (HVP). The proposed method covers the mismatch between the actual scene and the rendered image based on HVP. The experimental results show that the proposed method yields an improved color-rendering performance compared to conventional methods.

Unique hue data and CIECAM02: A comment on Xiao et al

AbstractThis letter to the editor notes the offsets from the origin of unique hue loci in the recent article, Unique Hue Data for Colour Appearance Models. Part III: Comparison with NCS Unique Hues by Xiao et al. [Color Res Appl 2015;40:256–263] and suggests that modifications to the cone response function in CIECAM02 be explored. © 2015 Wiley Periodicals, Inc. Col Res Appl, 2015

Color Rendering Model for High Dynamic Range Images Using a Tone Mapping Method and a Cone Response Function

Color Rendering Model for High Dynamic Range Images Using a Tone Mapping Method and a Cone Response Function

Color Rendering Model for High Dynamic Range Images Using a Tone Mapping Method and a Cone Response Function

Color Rendering Model for High Dynamic Range Images Using a Tone Mapping Method and a Cone Response Function

Tone-mapping and dynamic range compression using dynamic cone response

Over the last several decades, the tone-mapping and color correction methods have been incorporated into the display device such as LCD/LED display device or the photographic print. However, conventional methods are based on Gray-World Assumption using spatial smoothing filter to estimate the local illumination component. For this reason, most of these methods have several problems like visual perception problem, scale the contrast, gray-out, color distortion (dominate color, halo-artifact) and so on. Accordingly, this paper presents a color correction method using the dynamic cone response function based on human visual perception. The proposed method consists of tone-mapping and dynamic cone response. The tone-mapped image is obtained by using chromatic and achromatic colors. Thereafter, the resultant image is processed through the dynamic cone response function, and it is to estimate the dynamic response of human eye as well as to deal with mismatch between a real scene and the rendered image. The experimental results show that the proposed method yields better performance of color correction over the conventional methods.

동적 원추 세포 응답을 이용한 높은 동적 폭을 갖는 영상 색상 보정 방법

최근 들어, 실제 환경 영상과 디스플레이 영상간의 인지적 불일치를 위해, 인간 시각 시스템을 흉내 낸 높은 동적 범위를 갖는 영상 촬영 기술이 LCD/LED 디스플레이 장치에 사용되고 있다. 그러나 HDR 영상에는 섬광 한계뿐만 아니라 국부 영상대비 등의 문제가 있다. 이러한 문제를 해결하기 위해 중심/주변 레티넥스, 다중 스케일 영상 레티넥스, 톤 매핑 방법, iCAM06등 여러 가지 색상 보정 방법들이 제안되었다. 그러나 기존의 방법들에서는 결과 영상내의 전반적으로 특정 색상 두드러짐 현상이 발생한다. 따라서 본 논문에서는 동적 원추 세포 응답을 이용한 영상 보정 방법을 제안한다. 제안하는 방법은 톤 매핑과 동적 원추 세포 응답으로 구성된다. 색도 성분과 비색도 성분의 선형적인 보간을 이용하여 톤 매핑을 수행하고, 톤 매핑의 결과 영상에서 동적 원추 세포 응답 함수를 이용하여 원추세포 응답을 획득한다. 획득된 원추세포 응답을 이용하여 색상 보정을 수행한다. 이는 인간 눈의 동적 응답을 예측함과 동시에 보정된 영상이 실제 환경과 최대한 일치하도록 한다. 실험 결과에서 제안한 방법이 기존의 방법에 비해 색상 보정 효과가 우수함을 보인다.

Where Do the Unique Hues Come from?

Where are we to look for the unique hues? Out in the world? In the eye? In more central processing? 1. There are difficulties looking for the structure of the unique hues in simple combinations of cone-response functions like (L − M) and (S − (L + M)): such functions may fit pretty well the early physiological processing, but they don’t correspond to the structure of unique hues. It may seem more promising to look to, e.g., Hurvich & Jameson’s ‘chromatic response functions’; but these report on psychophysical behaviour, not on underlying physiology. So ‘opponent processing’ isn’t any particular help on the unique hues—and even physiology in general seems not to have come up with any good correlate or explanation. 2. Wright (Review of Philosophy and Psychology 2: 1–17, 2011) looks in a different place: to (a) magnitude of total visual response that a stimulus light provokes, the maxima and minima of which, he thinks, give us the boundaries of the main hue categories (Wright connects these with Thornton’s ‘prime’ and ‘antiprime’ colours in an illuminant: Journal of the Optical Society of America 61 (1971): 1155–1163); and (b) the ratio of chromatic to achromatic response, the maxima and minima of which, he suggests, give us the focal points of the unique hues. The suggestions are extremely interesting; but the desired correspondences have some counterexamples; and where they hold, one could wonder how much they depend upon the particular choice of functions to measure (a) and (b); and one could hope for more of an explanatory linkage between the sets of items in question. 3. Could the unique hues come from, so to speak, the external world? White and black can easily be defined as particular kinds of reflectance. What of the standard four unique hues? Variation in kinds of sunlight and skylight coincides well with variation along a line from unique yellow to unique blue (cf. Shepard 1992, Mollon 2006). If we wanted something to calibrate our standards for unique yellow and blue as the lens of the eye changes with age, and despite interpersonal cone differences, this would be a good basis—and there are several ways this can be extended to surface colours. But is there any essential connection between these things: is there any rationale why the light of the sun and the sky should be counted as unique hued? An answer may be: because in our environment, these illuminants are as close to white (or the natural illuminant colour) as you can get—to see things tinged with sunlight or skylight should be to see them minimally tinged with any alien colour. Whereas other hues in an illuminant would be treated as tinging with a more alien colour the thing seen.

Dynamic cone response functions for models of colour appearance

AbstractThe CIECAM97s type of colour appearance models results in some changes in hue and saturation for series of colours of constant chromaticity but changing luminance factor. To keep hue and saturation constant for such series, a model in which the dynamic cone response is based on a power function has been developed. By optimizing its parameters, its correlates of hue, lightness, colourfulness, brightness, and saturation perform nearly as well as those of CIECAM97s. A similar performance is achieved in a modified power model, which is more physiologically plausible. © 2003 Wiley Periodicals, Inc. Col Res Appl, 28, 82–88, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.10128