In the DCT/DPCM hybrid coding for images, the performance variation due to system parameters is investigated and several methods are tried for improvements of compression ratio, complexity and processing time.
The prediction coefficients are found to be less sensitive than the quantizer type and the normalization factors. Appropriate selection of these parameters improves the performance as much as 3dB in NMSE, but it doesn't improve the quality of reconstructed image that much.
In order to prevent the performance degradation due to the nostationarity of the real image data, a block-adaptive hybrid coding and a statistics-adaptive hybrid coding have been studied. These systems are found to improve the performance as much as 5dB in NMSE and to give a better quality of reconstructed image at high bit rates.
In order to have a good performance at low bit rates and to achieve a faster processing, a line adaptive hybrid coding and a block adaptive hybrid coding based on generalized covariance model have been proposed newly. The former is shown to provide 3dB gain over the non-adaptive system, and the latter gives subjectively a better quality of reconstructed image.
Also the performance degradation of these systems due to the channel noise has been studied.
