Image Processing for Cinema presents a detailed overview of image processing techniques that are used in practice in digital cinema. The book shows how image processing has become ubiquitous in movie-making, from shooting to exhibition. It covers all the ways in which image processing algorithms are used to enhance, restore, adapt, and convert moving images. These techniques and algorithms make the images look as good as possible while exploiting the capabilities of cameras, projectors, and displays. The author focuses on the ideas behind the methods, rather than proofs and derivations. The first part of the text presents fundamentals on optics and color. The second part explains how cameras work and details all the image processing algorithms that are applied in-camera. With an emphasis on state-of-the-art methods that are actually used in practice, the last part describes image processing algorithms that are applied offline to solve a variety of problems. The book is designed for advanced undergraduate and graduate students in applied mathematics, image processing, computer science, and related fields.
It is also suitable for academic researchers and professionals in the movie industry.
LIGHTS Light and Color Light as color stimulus Matching colors The first standard color spaces Perceptual color spaces Color appearance Optics Introduction Ray diagrams Reflection and refraction Lenses Optical aberrations Basic terms in photography CAMERA Camera Image processing pipeline Image sensors Exposure control Focus control White balance Color transformation Gamma correction and quantization Edge enhancement Output formats Additional image processing The order of the stages of the image processing pipeline ACTION Compression Introduction How is compression possible? Image compression with JPEG Image compression with JPEG2000 Video compression with MPEG-1, MPEG-2, MPEG-4 AVC (H.264) In-camera compression Denoising Introduction Classic denoising ideas Non-local approaches An example of a non-local movie denoising algorithm New trends and optimal denoising Denoising an image by denoising its curvature image Demosaicking and Deinterlacing Introduction Demosaicking Deinterlacing White Balance Introduction Human color constancy Computational color constancy under uniform illumination Retinex and related methods Cinema and colors at night Image Stabilization Introduction Rolling shutter compensation Compensation of camera motion Zoom-In and Slow Motion Introduction Zoom-in Slow-motion generation Transforming the Color Gamut Introduction Color gamuts Gamut reduction Gamut extension Validating a gamut mapping algorithm An example of a spatial gamut reduction algorithm Final remarks High Dynamic Range Video and Tone Mapping Introduction High dynamic range imaging Tone mapping Optimization of TM operators Final remarks Stereoscopic 3D Cinema Introduction Depth perception Making S3D cinema Parallax and convergence Camera baseline and focal length Estimating the depth map Changing the baseline/synthesizing a new view Changing the focus and the depth of field Factors of visual discomfort Color Matching for Stereoscopic Cinema Introduction Related work The algorithm Morphing the target image Aligning the histograms Propagating the colors to unmatched pixels Examples and comparisons Inpainting Introduction Video inpainting for specific problems Video inpainting in a general setting Video inpainting for stereoscopic 3D cinema Final remarks Bibliography Index