This paper presents an auto-white balancing algorithm named scoring. The spectral distributions of the Macbeth reference colors together with the spectral distributions of various color temperature light sources are used to obtain a number of reference color points in the CbCr color space. A number of representative color points are also obtained from a captured image by using a previously developed multi-scale clustering algorithm. A match is then established between the set of reference colors and the set of representative colors. The matching scheme generates the most likely light source candidate under which the image is taken. Furthermore, this paper presents an auto-exposure algorithm using a mapping from the luminance histograms of five sub- areas in the image to an exposure value. A neural network is designed to perform the mapping. The histogram in each sub- area is used to determine the mean, variance, minimum, and maximum luminance for that sub-area. The same spatial information is computed for previous frames to incorporate temporal changes in luminance into the network.
KEYWORDS: Digital signal processing, Charge-coupled devices, Algorithm development, Control systems, Camera shutters, Image processing, Light sources and illumination, Analog electronics, Cameras, Photography
This paper presents the real-time implementation of an auto- white-balancing and an auto-exposure algorithm on the TI TMS320DSC21 platform. This platform is a power-efficient single-chip processor that has been specifically designed for digital still cameras. Its architecture consists of five subsystems including an ARM micro-controller, a DSP core, a memory subsystem, two co-processors, and an imaging peripherals subsystem. Due to the memory constraints, the algorithms were modified to allow their real-time implementation of the processor, i.e. a processing rate of 30 frames per second. These modifications are discussed in the paper. The details of the algorithms are reported in an accompanying paper presented in the SPIE conference 4669B.
KEYWORDS: Prototyping, Distortion, Digital signal processing, Image compression, Algorithm development, RGB color model, Visualization, Color image processing, Image processing, Image processing algorithms and systems
This paper presents a modification of the multi-scale clustering algorithm in order to reduce the color content of an image in an automatic manner without requiring the specification of the number of colors. The clustering space is chosen to be the YCbCr color space as used in the JPEG compression standard. Although multi-scale clustering is capable of defining prominent data clusters in an automatic manner, it may not generate all perceptibly distinctive colors when applied to the YCbCr color space. In this work, we have modified the multi-scale clustering algorithm to overcome this limitation for color reduction purposes. Our color reduction algorithm consists of two parts. The first part is a modification of multi-scale clustering to obtain a set of primary color prototypes. The second part is a color sectoring method to obtain a set of secondary color prototypes. The developed color reduction algorithm has been implemented on a high performance DSP processor, namely TMS320C6201, due to the computational requirement associated with multi-scale clustering. The result show on average 50 percent more compression over that of the JPEG standard for the color portion of images with comparable levels of color distortion.
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