Unsupervised Color Film Restoration Using Adaptive Color Equalization

Chemical processing of celluloid based cinematic film, becomes unstable with time, unless they are stored at low temperatures. Some defects, such as bleaching on color movies, are difficult to solve using photochemical restoration methods. In these cases, a digital restoration tool can be a very convenient solution. Unfortunately, for old movies color and dynamic range digital restoration is usually dependent on the skill of trained technicians who are able to control the parameters through color adjustment, and may be different for a sequence or group of frames. This leads to a long and frustrating restoration process. As an alternative solution, we present in this paper, an innovative technique based on a model of human color perception:, to correct color and dynamic range with no need of user supervision and with a very limited number of parameters. The method is combined with a technique that is able to split the movie into different shots and to select representative frames (key frames) from each shot. By default, key frames are used to set the color correction method parameters that are then applied to the whole shot. Due to the robustness of the color correction method the setting used for the key frame is used successfully for all the frames of the same shot.

[1]  Carlo Gatta,et al.  From Retinex to Automatic Color Equalization: issues in developing a new algorithm for unsupervised color equalization , 2004, J. Electronic Imaging.

[2]  Yueting Zhuang,et al.  Adaptive key frame extraction using unsupervised clustering , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[3]  Warnakulasuriya Anil Chandana Fernando,et al.  Fade-in and fade-out detection in video sequences using histograms , 2000, 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353).

[4]  Yoshinobu Tonomura,et al.  VideoMAP and VideoSpaceIcon: tools for anatomizing video content , 1993, INTERCHI.

[5]  Andreas Girgensohn,et al.  Time-Constrained Keyframe Selection Technique , 2004, Multimedia Tools and Applications.

[6]  Bernard Besserer,et al.  Recent progress in automatic digital restoration of color motion pictures , 2001, IS&T/SPIE Electronic Imaging.

[7]  Dmitry Chetverikov,et al.  A Simple and Efficient Algorithm for Detection of High Curvature Points in Planar Curves , 2003, CAIP.

[8]  Arding Hsu,et al.  Image processing on compressed data for large video databases , 1993, MULTIMEDIA '93.

[9]  Alan Hanjalic,et al.  A New Method for Key Frame Based Video Content Representation , 1998, Image Databases and Multi-Media Search.

[10]  Takafumi Miyatake,et al.  IMPACT: an interactive natural-motion-picture dedicated multimedia authoring system , 1991, CHI.

[11]  In So Kweon,et al.  A New Technique for Shot Detection and Key Frames Selection in Histogram Space , 2000 .

[12]  林行刚,et al.  Key Frame Extraction Using Unsupervised Clustering Based on a Statistical Model , 2005 .

[13]  Raimondo Schettini,et al.  Quicklook2: An Integrated Multimedia System , 2001, J. Vis. Lang. Comput..

[14]  Bernard Besserer,et al.  Latest results in digital color film restoration , 2002 .

[15]  Alex Pentland,et al.  Video and Image Semantics: Advanced Tools for Telecommunications , 1994, IEEE Multim..

[16]  Thomas S. Huang,et al.  Exploring video structure beyond the shots , 1998, Proceedings. IEEE International Conference on Multimedia Computing and Systems (Cat. No.98TB100241).

[17]  Carlo Gatta,et al.  A new algorithm for unsupervised global and local color correction , 2003, Pattern Recognit. Lett..