Gamut Constrained Illuminant Estimation

This paper presents a novel solution to the illuminant estimation problem: the problem of how, given an image of a scene taken under an unknown illuminant, we can recover an estimate of that light. The work is founded on previous gamut mapping solutions to the problem which solve for a scene illuminant by determining the set of diagonal mappings which take image data captured under an unknown light to a gamut of reference colours taken under a known light. Unfortunately a diagonal model is not always a valid model of illumination change and so previous approaches sometimes return a null solution. In addition, previous methods are difficult to implement. We address these problems by recasting the problem as one of illuminant classification: we define a priori a set of plausible lights thus ensuring that a scene illuminant estimate will always be found. A plausible light is represented by the gamut of colours observable under it and the illuminant in an image is classified by determining the plausible light whose gamut is most consistent with the image data. We show that this step (the main computational burden of the algorithm) can be performed simply, quickly, and efficiently by means of a non-negative least-squares optimisation. We report results on a large set of real images which show that it provides excellent illuminant estimation, outperforming previous algorithms

A Parallel Error Diffusion Implementation on a GPU

In this paper, we investigate the suitability of the GPU for a parallel implementation of the pinwheel error di�usion. We demonstrate a high-performance GPU implementation by efficiently parallelizing and unrolling the image processing algorithm. Our GPU implementation achieves a 10-30x speedup over a two-threaded CPU error di�ffusion implementation with comparable image quality. We have conducted experiments to study the performance and quality tradeoff�s for di�fferences in image block sizes. We also present a performance analysis at assembly level to understand the performance bottlenecks

A parallel error diffusion implementation on a GPU

In this paper, we investigate the suitability of the GPU for a parallel implementation of the pinwheel error di�usion. We demonstrate a high-performance GPU implementation by efficiently parallelizing and unrolling the image processing algorithm. Our GPU implementation achieves a 10-30x speedup over a two-threaded CPU error di�ffusion implementation with comparable image quality. We have conducted experiments to study the performance and quality tradeoff�s for di�fferences in image block sizes. We also present a performance analysis at assembly level to understand the performance bottlenecks

Gamut Constrained Illuminant Estimation

This paper presents a novel solution to the illuminant estimation problem: the problem of how, given an image of a scene taken under an unknown illuminant, we can recover an estimate of that light. The work is founded on previous gamut mapping solutions to the problem which solve for a scene illuminant by determining the set of diagonal mappings which take image data captured under an unknown light to a gamut of reference colours taken under a known light. Unfortunately a diagonal model is not always a valid model of illumination change and so previous approaches sometimes return a null solution. In addition, previous methods are difficult to implement. We address these problems by recasting the problem as one of illuminant classification: we define aprioria set of plausible lights thus ensuring that a scene illuminant estimate will always be found. A plausible light is represented by the gamut of colours observable under it and the illuminant in an image is classified by determining the plausible light whose gamut is most consistent with the image data. We show that this step (the main computational burden of the algorithm) can be performed simply, quickly, and efficiently by means of a non-negative least-squares optimisation. We report results on a large set of real images which show that it provides excellent illuminant estimation, outperforming previous algorithms. 1