Color Correction for Tone Reproduction

High dynamic range images require tone reproduction to match the range of values to the capabilities of the display. For computational reasons as well as absence of fully calibrated imagery, rudimentary color reproduction is often added as a postprocessing step rather than integrated into the tone reproduction algorithm. However, in the general case this currently requires manual parameter tuning, although for some global tone reproduction operators, parameter settings can be inferred from the tone curve. We present a novel and fully automatic saturation correction technique, suitable for any tone reproduction operator, which exhibits better color reproduction than the state-ofthe-art and we validate its comparative effectiveness through psychophysical experimentation. Introduction Recent advances in both capture and display technologies allow images of a much wider dynamic range to be photographed, manipulated and displayed, better capturing the light of natural scenes and giving artists unparalleled freedom. Unlike prevalent consumer imaging pipelines though, no high dynamic range (HDR) standard has yet emerged defining the precise range, format or encoding to be used. As such, HDR data often needs to be compressed for display on most current displays, a process known as tonemapping [15, 2]. The aim of this paper is to preserve the appearance and information content of the image as much as possible while ensuring that it can be displayed on the chosen display device. To achieve that, tonemapping algorithms typically operate on the luminance of the image with little to no consideration for the color information present, leading to noticeable changes in the color appearance of the image, as shown in Figure 1. Commonly, tone compressed images acquire an over-saturated appearance when only the luminance channel is processed [12, 18]. Image appearance models, which can be seen as tone reproduction operators with integrated color appearance management [7, 9, 16], aim to reproduce color appearance, but they are designed with calibrated applications in mind and often come at the cost of higher computational complexity due to spatially varying processing. Despite their accuracy, these factors can limit their general applicability. Some solutions exist for correcting saturation mismatches after tone reproduction [12, 18]. This leads to computationally efficient correction, although we have observed that existing methods tend to create hue and luminance artefacts. Moreover, they require manual parameter selection which is strongly image and tone reproduction operator dependent. Recently, a psychophysical study was conducted for defining an automatic model to derive the parameters necessary for such corrections, but only allows parameters to be predicted when the tone compression or expansion function is global [12]. Instead, we propose a new approach for correcting saturation mismatches after dynamic range compression. We base our algorithm on insights from color science and on the observation that the amount of desaturation can be inferred from the non-linearity applied by the tone curve, irrespective of whether the tone reproduction operator was spatially varying or not. As such, our approach is parameter-free and agnostic to the operator used for mapping the dynamic range of the image or video. We find that our algorithm reproduces saturation significantly better than the current state-of-the-art. Related Work Differences in viewing conditions may result in significant mismatches in perceived color, which can be attributed to idiosyncrasies of the human visual system. To ensure that the appearance of a scene is correctly reproduced on a display, many issues will have to be taken into account, all broadly belonging to the field of color reproduction [8]. Image appearance models can be used to reproduce images as a human observer would see them under given viewing conditions [5, 16]. Such algorithms can be configured to yield calibrated color reproduction, and therefore do not require color post-processing. However, measurements of scene and display conditions are needed as inputs to image appearance models so that the human visual response can be accurately predicted. This requires specialist equipment such as photometers. These algorithms also tend to be computationally expensive, further limiting their use to offline processing. Dynamic range mismatches between scenes and display devices are therefore typically handled by tone reproduction operators. In essence, most of these algorithms focus on one dimension of the color gamut, namely compression along the luminance direction [15, 2]. Appearance effects are often ignored, leading to images which may appear too saturated. This problem can be mitigated by combining tone reproduction and color appearance algorithms [1]. However, this solution still requires calibrated data and measured viewing conditions to drive the color appearance component. A more common approach to saturation reproduction is to post-process the tone-mapped image, manually adjusting saturation to levels that appear plausible. Perhaps the most well-known technique for color correction involves the adjustment of color values by means of a power function, according to user parameter p ∈ [0,1] [18]. Given an original high dynamic range imTonemapped with Li et al. 2005 Corrected saturation reduced Corrected saturation enhanced Tonemapped with Reinhard et al. 2012 Figure 1. The same HDR image was tonemapped with different operators (left [10], right [16]). The left tonemapped image is overly saturated, while the tonemapping algorithm used on the right has reduced the saturation too far. With our method, both images are automatically corrected to have a very similar appearance by considering their relation with the original HDR image. (Source image from Mark Fairchild’s HDR Survey) age with input pixels Mo = (Ro,Go,Bo) specified in some linear RGB color space, and its associated per-pixel luminances Lo, it is first tonemapped with an operator f () that modifies the image’s luminances, Lt = f (Lo). The color-corrected image Mc is then produced with: