Digital and mobile imaging camera system performance is determined by a combination of sensor characteristics, lens characteristics, and image-processing algorithms. As pixel size decreases, sensitivity decreases and noise increases, requiring a more sophisticated noise-reduction algorithm to obtain good image quality. Furthermore, small pixels require high-resolution optics with low chromatic aberration and very small blur circles. Ultimately, there is a tradeoff between noise, resolution, sharpness, and the quality of an image. This short course provides an overview of "light in to byte out" issues associated with digital and mobile imaging cameras. The course covers, optics, sensors, image processing, and sources of noise in these cameras, algorithms to reduce it, and different methods of characterization. Although noise is typically measured as a standard deviation in a patch with uniform color, it does not always accurately represent human perception. Based on the "visual noise" algorithm described in ISO 15739, an improved approach for measuring noise as an image quality aspect will be demonstrated. The course shows a way to optimize image quality by balancing the tradeoff between noise and resolution. All methods discussed will use images as examples.

Digital imaging system resolution is determined by a combination of sensor characteristics, lens characteristics, and image-processing algorithms. As pixel size decreases, sensitivity decreases and noise increases, requiring a more sophisticated noise-reduction algorithm to obtain good image quality. Furthermore, small pixels require high-resolution optics with low chromatic aberration and very small blur circles. Ultimately, there is a tradeoff between noise, resolution, sharpness, and the quality of an image. This short course summarizes the sources of noise, algorithms to reduce it, and different methods of characterization. Although noise is typically measured as a standard deviation in a patch with uniform color, it does not always accurately represent human perception. Based on the "visual noise" algorithm described in ISO 15739, an improved approach for measuring noise as an image quality aspect will be demonstrated. The course shows a way to optimize image quality by balancing the tradeoff between noise and resolution. All methods discussed will use images as examples.

When a digital image is captured using a digital still camera, DSC, it needs to be processed. For consumer cameras this processing is done within the camera and covers various steps like dark current subtraction, flare compensation, shading and color compensation, demosaicing, white balancing, tonal and color correction, sharpening, and compression. All of these steps have a significant influence on image quality so it is important to know how image quality can be measured and what standardized methods exist. The course provides the basic methods for each step of the imaging pipeline. While we run several images through a sample pipeline we will alter the algorithms to discover the visual differences and the differences in the measured values using the various test methods. This helps to understand the process and provides a lot of information on how to increase the over all image quality. The course topics include basic review of the image processing pipeline; explanation of the different steps and their basic algorithms; practical image processing using sample images and software; introduction to image quality analysis; discussion on test scenes and visual image analysis; measurement of different image quality aspects like OECF, Dynamic Range, Noise, Resolution, Color Reproduction; explanation of the available free and commercial software; and demonstration of illuminator, test chart, and software based measurements.

When an image is captured using a digital imaging device, it needs to be rendered. For consumer cameras this processing is done within the camera, and covers various steps like dark current subtraction, flare compensation, shading and color compensation, demosaicing, white balancing, tonal and color correction, sharpening, and compression. All of these steps have a significant influence on image quality, so to design and tune these algorithms it is important to know how image quality can be measured and what standardized methods exist as well as their pros and cons. The course provides the basic methods for all steps of the imaging pipeline which involve color. Participants will get to examine the basic algorithms that exist and evaluate images processed through a sample pipeline. We will see how image data influences color transforms and white balance. This helps to understand the process and provides substantial information on how to increase the overall image quality. Finally, we will look at how non-ideal hardware affects the quality of the output image. Examples include non-ideal spectral filters, sensor crosstalk, spectral responsivity mismatch, etc.