Jarno Nikkanen

Subjective effects of white-balancing errors in digital photography

Automatic white-balancing algorithms play a key role in digital photography. Failure to estimate illumination chromaticity correctly will result in an invalid overall color cast in the final image. Such image-wide color casts are easily detected by human observers. The aim of this paper is to quantify the subjective effects of white-balancing errors in digital photography. It is achieved by means of subjective tests. Different natural images are utilized in order to study the effect of image contents on the acceptability of white point shifts. Multiple directions of white point error are covered in order to study the relation between image contents and the most acceptable direction of white point shift. Details of the camera system and sensor used are also discussed in order to separate the contribution of white balancing errors from the contribution of other color processing to the final color errors. Typically the performance of automatic white-balancing algorithms is presented in the literature by using different color or color difference measures. This paper provides a means to interpret those results from the point of view of digital photography, namely, to separate significant white-balancing errors from insignificant ones.

Applying Visual Object Categorization and Memory Colors for Automatic Color Constancy

This paper presents a framework for using high-level visual information to enhance the performance of automatic color constancy algorithms. The approach is based on recognizing special visual object categories, called here as memory color categories, which have a relatively constant color (e.g. the sky). If such category is found from image, the initial white balance provided by a low-level color constancy algorithm can be adjusted so that the observed color of the category moves toward the desired color. The magnitude and direction of the adjustment is controlled by the learned characteristics of the particular category in the chromaticity space. The object categorization is performed using bag-of-features method and raw camera data with reduced preprocessing and resolution. The proposed approach is demonstrated in experiments involving the standard gray-world and the state-of-the-art gray-edge color constancy methods. In both cases the introduced approach improves the performance of the original methods.

Color constancy by characterization of illumination chromaticity

Computational color constancy algorithms play a key role in achieving desired color reproduction in digital cameras. Failure to esti- mate illumination chromaticity correctly will result in invalid overall colour cast in the image that will be easily detected by human observers. A new algorithm is presented for computational color constancy. Low com- putational complexity and low memory requirement make the algorithm suitable for resource-limited camera devices, such as consumer digital cameras and camera phones. Operation of the algorithm relies on char- acterization of the range of possible illumination chromaticities in terms of camera sensor response. The fact that only illumination chromaticity is characterized instead of the full color gamut, for example, increases robustness against variations in sensor characteristics and against failure of diagonal model of illumination change. Multiple databases are used in order to demonstrate the good performance of the algorithm in com- parison to the state-of-the-art color constancy algorithms. C 2011 Society of

Generic software framework for a line-buffer-based image processing pipeline

The majority of the current affordable mobile devices contain a camera with simple optics and a low-cost camera sensor. In these devices, the quality of the captured images is made acceptable with various image processing algorithms that together form an image reconstruction pipeline. The best performance is often achieved with a hardware pipeline, but software implementations can be preferred to minimize production costs and to maximize flexibility. This paper presents a generic software framework for a line-buffer-based image reconstruction pipeline. The presented framework is capable of operating in low-memory environments and significantly eases algorithm insertions, changes of processing order, and other pipeline management tasks. The savings in development time can be even months. In addition, our experiments show that it offers over 99% memory savings compared with traditional implementations using a ping-pong buffer scheme with full-sized image buffers. The implemented framework also enhances processing performance due to better cache usage and increases flexibility with various pipeline configurations.

The Effect of Motion Blur and Signal Noise on Image Quality in Low Light Imaging

Motion blur and signal noise are probably the two most dominant sources of image quality degradation in digital imaging. In low light conditions, the image quality is always a tradeoff between motion blur and noise. Long exposure time is required in low illumination level in order to obtain adequate signal to noise ratio. On the other hand, risk of motion blur due to tremble of hands or subject motion increases as exposure time becomes longer. Loss of image brightness caused by shorter exposure time and consequent underexposure can be compensated with analogue or digital gains. However, at the same time also noise will be amplified. In relation to digital photography the interesting question is: What is the tradeoff between motion blur and noise that is preferred by human observers? In this paper we explore this problem. A motion blur metric is created and analyzed. Similarly, necessary measurement methods for image noise are presented. Based on a relatively large testing material, we show experimental results on the motion blur and noise behavior in different illumination conditions and their effect on the perceived image quality.

Blemish detection in camera production testing using fast difference filtering

In the camera manufacturing, special methods are needed to detect blemishes occurring on the camera sensor pixels. A blemish is referred as a region of pixels in the camera sensor that are somewhat darker than the background. The blemishes are difficult to detect accurately, but on the other hand, they cause a significant reduction in camera quality. We present a novel filtering method for the blemish detection. The method is based on image scaling, filtering, and difference image calculation that is very fast and accurate in the detection of blemishes. In addition, the algorithm can cope with unprocessed raw image data, in which various distortions, such as noise and vignetting, can be present.