A Novel Automatic White Balance Method For Digital Still Cameras

Transcription

1 A Novel Automatic White Balance Method Fo Digital Still Cameas Ching-Chih Weng 1, Home Chen 1,2, and Chiou-Shann Fuh 3 Depatment of Electical Engineeing, 2 3 Gaduate Institute of Communication Engineeing Depatment of Compute Science and Infomation Engineeing National Taiwan Univesity, Taipei, Taiwan @ntu.edu.tw, home@cc.ee.ntu.edu.tw, fuh@csie.ntu.edu.tw Astact Automatic white alance is an impotant function of digital still cameas. The goal of white alance is to adjust the image such that it looks as if it is taken unde canonical light. We poposed a novel technique to detect efeence white points in an image. Ou algoithm uses dynamic theshold fo white point detection and is moe flexile than othe existing ad hoc algoithms. We have tested the algoithm on 50 images taken unde vaious light souces. The esults show that the algoithm is supeio o compaale to othe methods in oth ojective and sujective evaluations. The complexity of the algoithm is quite low, which makes it attactive fo eal-wold applications. I. INTRODUCTION When an image of a scene is captued y a digital camea, the senso esponse at each pixel depends on the illumination [1]. That is, each pixel value ecoded y the senso is elated to the colo tempeatue of the light souce. When a white oject is illuminated unde a low colo tempeatue, it will appea eddish in the ecoded image. Similaly, it will appea luish unde a high colo tempeatue. The goal of white alance is to pocess the image such that it looks as if it is taken unde canonical light [3], [6]. Geneally, white alance algoithms consist of two steps. They fist estimate the illumination, and then use the esult otained to compensate the image [4]. To maintain the colo constancy of an image taken unde diffeent light souces, computational colo constancy algoithms have een applied to accomplish white alance fo digital cameas. Thee ae vaious computational colo constancy algoithms [1], [2], [4], [5], [8] poposed in the liteatue, including gey wold, pefect eflecto, gamut mapping, and colo y coelation. Most of these algoithms make cetain assumptions of the colo distiution of the image. They diffe in the way the illumination is estimated. Illumination estimation is a challenging issue ecause the senso esponse is contolled y many diffeent factos such as oject shape, illumination geomety, etc. These factos ae difficult to sepaate in the illumination estimation step. In the image adjustment step, methods such as the Von Kies diagonal model can e used to scale the R, G, and B channels sepaately [4], [7]. The diagonal model holds when the fequency esponses of R, G, and B components ae naow and and do not ovelap with each othe, and the eo of diagonal model is much smalle than that of the illumination estimation [4]. In this pape, we pesent a novel technique that uses image statistics instead of ad hoc assumptions to estimate the efeence white points. Ou method pefoms ette than existing algoithms in chomaticity test. II. EXISTING AUTOMATIC WHITE BALANCE ALGORITHMS In this section we will discuss seveal popula existing automatic white alance algoithms. A. Gay Wold Method The gay wold algoithm woks unde the assumption that, given an image with sufficient colo vaiations, the aveage of eflectance of a scene is achomatic [4]. The gay wold method is one of the oldest and simplest and is still a popula technique. It asically woks well. Howeve it fails when thee ae lage ojects with unifom colo in an image

2 B. Pefect Reflecto Method The pefect eflecto algoithm is ased on the assumption that the ightest pixel in an image coesponds to an oject point on a glossy o specula suface, which conveys a geat amount of infomation aout the illumination of the scene [4]. Specula o glossy sufaces eflect the actual colo of the light souce ecause thei eflectance functions ae constant ove a wide ange of wavelengths. The pefect eflecto algoithm exploits this popety fo image adjustment. It locates the ightest pixel and assigns it as the efeence white point. C. Fuzzy Rule Method In the fuzzy ule method (FRM) [3], the image is analyzed in the C C colo space. Fig. 1 shows the deviations of vaious colos fom thei nominal positions unde diffeent light souces. It is also found that ight colos deviate moe than dak colos and that the C to C atio of the white oject is etween -1.5 to Seveal fuzzy ules ae set ased on the chaacteistics discussed aove. FRM at fist divides the image into 8 segments. The aveages of C and C within each segment ae calculated. Then the weighting factos fo each segments ae detemined, ased on the fuzzy contol means, to calculate the evaluating C and C of the whole image fame. The C and C indicate the deviation of the image colo fom the white alance point. These values ae employed to otain the s fo C and C adjustment of each pixel. The pocedue stated aove will e pefomed iteatively until the esults of C and C ae close to that of white alance point [3]. Fig. 1. The colo deviation unde high and low colo tempeatue in the C C space. A: The deflected diection of high colo tempeatue. B: The deflected diection oflow colo tempeatue. D. Chikane's Method Chikane's method [6] is ased on the concept of pe-pocessing. This method fist applies histogam equalization on the input image to enhance the contast of the image pixels. Then it detemines the efeence white points y using pe-defined theshold values. Chikane's method pefoms well fo most images ut it degades when images have a elatively small nume of white points. III. OUR PROPOSED METHOD In ou method, we use a dynamic theshold (as opposed to pedefined theshold in pevious appoaches) to detect white points in an image. Simila to pevious appoaches, ou method consists of two steps: white point detection and white point adjustment. Fist, the image is conveted fom RGB to YCC colo space. Based on the colo chaacteistics illustated in Fig. 1, we define a nea-white egion that consists of the candidate efeence white points, as shown in Fig. 2(a). The candidate - 5 -

3 efeence white points ae chosen as follows: Fist we calculate the mean values M and M of and C, espectively. Then we calculate the aveage asolute diffeences D and D as follows: ( ) D = C (, i j) M / N i, j (1) C ( ) D = C (, i j) M / N i, j (2) Whee C (, i j ) and C (, i j ) ae the chomaticity values of pixel ( i, j), and N is the nume of pixels used in calculation. To enhance the oustness of ou method, we patition the image into 12 egions, as shown in Fig. 2(), and calculate M, M, D, and D fo each egion. If the D and D values of a egion ae too small, the egion is discaded ecause it does not have enough colo vaiation. This additional step helps peventing lage unifom ojects fom affecting the esult. The final M, and M, D, and D ae otained y taking the aveage of those egions that pass this additional step. The nea-white egion is composed of pixels that satisfy the following elationships: C (, i j) ( M + D sign( M )) < 1.5 D (3) C ( i, j) (1.5 M + D sign( M )) < 1.5 D (4) (a) Fig. 2. (a) The nea white egion of the image in C () C space. () The 12 divisions of an image. The idea ehind these equations is simila to that of the gay wold method. That is, the mean values of chomaticity, M and M, manifest the colo deviation of whole image. Howeve, the white colo deviate the most in esponse to diffeent colo tempeatues, as can e seen fom Fig.1. This is why we intoduce D, and D in the two equations. Then, ased on the ightness value, the top 10% of the candidate efeence white points in neawhite egion ae selected as efeence white points. Fig. 3 shows an example esult of this pocess. A, the nea white egion is the collection of ( C, C ) s whose coesponding pixels satisfy (3) and (4)

4 (a) () (c) Fig. 3. Aquaium image. (a) The oiginal image. () Non-lack pixels in the image coespond to memes of the nea-white egion in the C C space. (c) Non-lack pixels in the image epesent efeence white points. (d) White alanced image geneated y ou method. Afte the efeence white points ae detemined, the Von Kies model is used to adjust the image. The channel s ae deived fom the mean values of the efeence white points. To maintain the luminance of the whole image at the same level, the maximum luminance value is used in deiving the channel s. The channel s R, G, and B ae computed y: max / (d) R = Y R (5) G = Y G (6) max / B = Y B (7) max / whee R, G, and B ae the mean values of the efeence white points fo the thee channels. Y max is the maximum luminance value of the image pixels. Accoding to the Von Kies diagonal model, the pixel value of each pixel in the image is adjusted y: R = R R (8) G = G G (9) B = B B (10) whee R, B, and G ae the oiginal values of pixels in the image, and R, B, and G ae the adjusted pixel values. IV. EXPERIMENTAL RESULTS We tested ou method ast Chikane s method, the fuzzy ule method (FRM), the gey wold method (GWM), and the pefect eflecto method (PRM). A total of 50 test images wee captued unde five diffeent light souces poduced in the la and unde seveal household light souces. The ojective evaluation method descied in [6] is used fo pefomance compaison. It is ased on the aveage chomaticity value ( 2 2 C C ) + of the achomatic patches of the ColoChecke

5 The oiginal pictue of ColoChecke taken unde daylight and the esult geneated y the five tested methods ae shown in Fig. 4. The test esults of a natual image ae shown in Fig. 5. (All test esults ae availale on ). (a) () (c) (d) (e) (f) Fig. 4. (a) The oiginal image unde daylight. ()-(f) Images geneated y ou method, Chikane s method, FRM, GWM, and PRM, espectively. (a) () (c) (d) (e) (f) Fig. 5. (a) The oiginal image. ()-(f) Images geneated y ou method, Chikane s method, FRM, GWM, and PRM, espectively. The esults of ojective evaluation ae summaized in Tale. 1, whee a lowe scoe means a ette esult. The esults show that ou method pefoms ette than the othe methods in most cases. The sujective evaluation shows that the image quality is much impoved afte applying ou method. The supeioity of ou method is ooted on the fact that ou method uses a dynamic theshold to detemine the efeence white points. It adapts to the image statistics and hence is ale to captue the efeence white points ette than othe techniques that use ad hoc ules

6 Tale 1. CHROMATICITY EVALUATIONS Image Oiginal GMW PRM FRM Chikane s Ous Daylight Coolwhite INCA TL Daylight H Hoizon Hoizon Coolwhite INCA V. CONCLUSIONS A novel automatic white alance method fo digital still cameas is descied. This method detects efeence white points y using the statistical chaacteistics of images. We have tested the algoithm on 50 images taken unde vaious light souces. Compaed to othe ad hoc techniques, ou method pefoms ette fo most cases in the ojective evaluation. The sujective test esults also show that the algoithm is supeio o compaale to othe methods. The complexity of the algoithm is quite low, thus it is suitale fo eal-wold implementation. ACKNOWLEDGMENT The authos would like to thank Ms. Vasha Chikane fo poviding the test images and the chomaticity evaluation softwae and fo offeing helpful discussions. REFERENCES [1] K. Banad, V. Cadei, and B. Funt, A compaison of computational colo constancy algoithms - pat I: methodology and expeiment with synthesized data, IEEE Tans. on Image Pocessing, vol. 11, pp , Sept [2] K. Banad, V. Cadei, and B. Funt, A compaison of computational colo constancy algoithms - pat II: expeiments with image data, IEEE Tans. on Image Pocessing, vol. 11, pp , Sept [3] Y.C. Cheng, W.H. Chen, and Y.Q. Chen, Automatic white alance fo digital still camea, IEEE Tans. Consume Electonics, vol. 41, pp , [4] K. Banad, Pactical colo constancy, PhD thesis, Simon Fase Univesity, School of Computing, [5] G.D. Finlayson, S.D. Hodley, and P.M. Huel, Colo y coelation: a simple, unifying famewok fo colo constancy, IEEE Tans. on Patten Analysis and Machine Intelligence, vol. 23, pp , Nov [6] V. Chikane, and C.S. Fu, Automatic white alance fo digital still camea, pesented in Confeence on Compute Vision Gaphics and Image Pocessing, Hualien, Taiwan, Aug [7] R.C. Gonzales, and R.E. Woods, Digital Image Pocessing, Addison Wesley, Reading, MA, [8] D. Fosyth, A novel algoithm fo colo constancy, Int. J. Comput. Vis., vol. 5, pp. 5-36,