Xerox Corporation

Kien T Nguyen

Uoc H Nguyen

Abraham E Claproth

Chia-Hao Lee

Thanh D Truong

Sang-Chul Kang

Arnold Hackett

Weili Lin

In a system where two types of data are to be compressed using hierarchical vector quantization (HVQ), it would be typical for there to be two compression tables, one optimized for each type of data. A boundary between the two types may occur within a block to be compressed. In this case, a third table, optimized for a block having both types of data, is used.

HVQ compression for image boundaries

Using hierarchical vector quantization (HVQ) to accomplish rotation in addition to compression and decompression. During the compression step the image is divided into segments and each segment is reduced to a codeword. These codewords are transposed so that the segments will be in rotated order when printed. Then the codewords are decompressed to form segments in which the pixels are in rotated order.

HVQ compression combined with orthogonal rotation

A compression process of dividing each word to be compressed into least and most significant bits, and applying a different compression ratio to each. If the compression processes are lossy, both parts of the output can also be losslessly compressed before being output. Hierarchical vector quantization can be issued for either lossy compressor.

Two channel HVQ (hierarchical vector quantization) compression

Printing hints can be efficiently compressed in a system using hierarchical compression (HVQ) by first using HVQ to generate compressed codewords, and then adding the printing hint to the codewords to generate an intermediate form of codeword, before sending this intermediate form to a pattern matching lossless compressor for final compression. Even though the same printing hint may be applied to a number of codewords, the overall compression will be affected very little by the addition of the printing hints since the hints are identical over a large number of codewords in series.

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Kien T Nguyen