The Coding-Optimal Transform

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 signi cant result of our approach is a new transform basis speci cally designed to minimize mean-squared quantization distortion for both xed-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) di ers 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 nds the COT for a given signal. In addition, we present image compression results d...

• Type: Conference
• Year: 2001
• Where: DCC
• Authors: Cynthia Archer, Todd K. Leen
• Keywords: Coding Optimal Transform, Computer Graphics, DCC 2001, Karhunen-Loeve Transform, Transform Coding Algorithm