@article{1436498,
      recid = {1436498},
      author = {Rychagov, Michael N., and Tolstaya, Ekaterina V., and  Sirotenko, Mikhail Y.,},
      title = {Smart algorithms for multimedia and imaging /},
      pages = {1 online resource (xiv, 433 pages) :},
      abstract = {This book presents prospective, industrially proven  methods and software solutions for storing, processing, and  viewing multimedia content on digital cameras, camcorders,  TV, and mobile devices. Most of the algorithms described  here are implemented as systems on chip firmware or as  software products and have low computational complexity and  memory consumption. In the four parts of the book, which  contains a total of 16 chapters, the authors address  solutions for the conversion of images and videos by  super-resolution, depth estimation and control and  mono-to-stereo (2D to 3D) conversion; display applications  by video editing; the real-time detection of sport  episodes; and the generation and reproduction of natural  effects. The practical principles of machine learning are  illustrated using technologies such as image classification  as a service, mobile user profiling, and automatic view  planning with dictionary-based compressed sensing in  magnetic resonance imaging. The implementation of these  technologies in mobile devices is discussed in relation to  algorithms using a depth camera based on a colour-coded  aperture, the animated graphical abstract of an image, a  motion photo, and approaches and methods for iris  recognition on mobile platforms. The book reflects the  authors' practical experience in the development of  algorithms for industrial R & D and the commercialization  of technologies. Explains digital techniques for digital  cameras, camcorders, TV, mobile devices; Offers essential  algorithms for the processing pipeline in multimedia  devices and accompanying software tools; Features advanced  topics on data processing, addressing current technology  challenges.},
      url = {http://library.usi.edu/record/1436498},
      doi = {https://doi.org/10.1007/978-3-030-66741-2},
}