Automated video chain optimization

Video processing algorithms found in complex video appliances such as television sets and set top boxes exhibit an interdependency that makes it is difficult to predict the picture quality of an end product before it is actually built. This quality is likely to improve when algorithm interaction is explicitly considered. Moreover, video algorithms tend to have many programmable parameters, which are traditionally tuned in manual fashion. Tuning these parameters automatically rather than manually is likely to speed up product development. We present a methodology that addresses these issues by means of a genetic algorithm that, driven by a novel objective image quality metric, finds high-quality configurations of the video processing chain of complex video products.

[1]  Michael Yuen,et al.  A survey of hybrid MC/DPCM/DCT video coding distortions , 1998, Signal Process..

[2]  Tatsuo Nakajima,et al.  Building audio and visual home appliances on commodity software , 2001, ICCE. International Conference on Consumer Electronics (IEEE Cat. No.01CH37182).

[3]  Erwin A. de Kock,et al.  YAPI: application modeling for signal processing systems , 2000, Proceedings 37th Design Automation Conference.

[4]  de G Gerard Haan,et al.  Video processing for multimedia systems , 2000 .

[5]  Larry J. Eshelman,et al.  The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination , 1990, FOGA.

[6]  C. Hentschel,et al.  Noise measurement in video images , 2000, 2000 Digest of Technical Papers. International Conference on Consumer Electronics. Nineteenth in the Series (Cat. No.00CH37102).

[7]  G. de Haan,et al.  IC for motion-compensated 100 Hz TV with natural-motion movie-mode , 1996 .

[8]  Nikolas P. Galatsanos,et al.  Projection-based spatially adaptive reconstruction of block-transform compressed images , 1995, IEEE Trans. Image Process..

[9]  Ron Shonkwiler,et al.  Parallel Genetic Algorithms , 1993, ICGA.

[10]  Rodger Lea,et al.  Networking Home Entertainment Devices with HAVi , 2000, Computer.

[11]  Cristina V. Lopes,et al.  Open Implementation Design Guidelines , 1997, Proceedings of the (19th) International Conference on Software Engineering.

[12]  Olukayode A. Ojo,et al.  An algorithm for integrated noise reduction and sharpness enhancement , 2000, 2000 Digest of Technical Papers. International Conference on Consumer Electronics. Nineteenth in the Series (Cat. No.00CH37102).

[13]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[14]  L. Scharf,et al.  Statistical Signal Processing: Detection, Estimation, and Time Series Analysis , 1991 .

[15]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[16]  Egbert G. T. Jaspers,et al.  A generic 2D sharpness enhancement algorithm for luminance signals , 1997 .

[17]  J.G.W.M. Janssen,et al.  An advanced sampling rate conversion technique for video and graphics signals , 1997 .

[18]  Prashant J. Shenoy,et al.  Application performance in the QLinux multimedia operating system , 2000, ACM Multimedia.

[19]  Tatsuo Nakajima,et al.  Towards universal software substrate for distributed embedded systems , 2001, Proceedings Sixth International Workshop on Object-Oriented Real-Time Dependable Systems.