## Abstract

We propose a new transform coding algorithm that integrates all optimization steps into a coherent and consistent framework. Each iteration of the algorithm is designed to minimize coding distortion as a function of both the transform and quantizer designs. Our algorithm is a constrained version of the LBG algorithm for vector quantizer design. The reproduction vectors are constrained to lie at the vertices of a rectangular grid. A significant result of our approach is a new transform basis specifically designed to minimize mean-squared quantization distortion for both fixed-rate and entropy-constrained coding. For Gaussian distributed data, this transform reduces to the Karhunen-Loeve transform (KLT). However, in general the coding optimal transform (COT) differs from the KLT enough to provide up to 1 dB improvement in compressed signal-to-noise ratio (SNR) on images. We describe a practical algorithm that finds the COT for a given signal. In addition, we present image compression results demonstrating the SNR improvement achieved with our algorithm relative to KLT based transform coding.

Original language | English (US) |
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Pages (from-to) | 381-390 |

Number of pages | 10 |

Journal | Data Compression Conference Proceedings |

State | Published - 2001 |

Externally published | Yes |

Event | Data Compression Conference - Snowbird, UT, United States Duration: Mar 27 2001 → Mar 29 2001 |

## ASJC Scopus subject areas

- Computer Networks and Communications