Translator Disclaimer
9 January 1998 Adaptive prediction models for optimization of video encoding
Author Affiliations +
Proceedings Volume 3309, Visual Communications and Image Processing '98; (1998)
Event: Photonics West '98 Electronic Imaging, 1998, San Jose, CA, United States
The increasing demand on high quality digital video broadcasting systems has driven the developments of several coding standards, the motion picture experts group (MPEG) standard 4 being the most recent. Since these standards only define the bitstream syntax, such essential features of the coder as controlling the output bitrate have to be implemented individually. An important problem arising here is the optimization of the bitrate-distortion trade-off, since in general the resulting bitstream has to meet the requirements of a communication channel with limited bandwidth. Obviously the output bitrate can be influenced by adjusting the quantization of the coder, but however a general functional relation to predict bitrate and distortion at a given quantization for a frame does not exist, since these parameters depend strongly on the properties of the actual video sequence. With the help of a predictor adapting on a specific video sequence, it would be possible to run an algorithm, which calculates the optimal quantization parameters causing the coder to meet the bitrate requirements of the given channel. Main objective of this paper is to design az model of a MPEG-4 coder with reduced complexity that is capable to predict the functional relation between quantization, bitrate and distortion for the next frame to be coded of a given video sequence. This model will be implemented as a neuronal network forming a two-layer perceptron.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Axel Brinkmann, Jose Ignacio Ronda, Angel Pacheco, and Narciso N. Garcia "Adaptive prediction models for optimization of video encoding", Proc. SPIE 3309, Visual Communications and Image Processing '98, (9 January 1998);


Back to Top