Edgebased automatic white balancing with linear illuminant constraint Automatic white balancing is an important function for digital cameras. It adjusts the color of an image and makes the image look as if it is taken under canonical light. White balance is usually achieved by estimating the chromaticity of the illuminant and then using the resulting estimate to compensate the image. The grey world method is the base of most automatic white balance algorithms. It generally works well but fails when the image contains a large object or background with a uniform color. The algorithm proposed in this paper solves the problem by considering only pixels along edges and by imposing an illuminant constraint that confines the possible colors of the light source to a small range during the estimation of the illuminant. By considering only edge points, we reduce the impact of the dominant color on the illuminant estimation and obtain a better estimate. By imposing the illuminant constraint, we further minimize the estimation error. The effectiveness of the proposed algorithm is tested thoroughly. Both objective and subjective evaluations show that the algorithm is superior to other methods.

Color and Illumination Compensation Scheme for Multiview Video Service This paper deals with the problem of color and illumination compensation for multiview video service. In particular, we focus on finding a simple and lightweight color compensation algorithm because it is more friendly for realtime service. In this paper, we developed multiview rendering software and applied and tested the diagonal matrix based color compensation method. Through experiments, we confirmed that there are advantages of diagonal color transfer model compared with histogram matching and statistical color transfer scheme. We also have experimentally observed that the approach works even when the multiview image is corrupted under realistic conditions such as rain or sunflare.