Variable quality image compression system based on

SPIHT

Authors: A. Jarvi, J. Lehtien, O. Nevalainen

Source: Signal Processing: Image Communication 14(1999) 683-696

Date: 11 September 1999

Speaker: Ming-Ni Wu

SPIHT

• Set Partitioning In Hierarchical Trees by Said and Pearlman 1996

• a wavelet-based image compressing method

• to provide better performance then original EZW(Embedded Zerotree Wavelet)

• a bitplane coding method

SPIHT

n: the number of significant bits in the coefficient table having the largest absolute value

H: set of coordinates of all spatial orientation tree roots

LSP: List of Significant pixels

LIP: List of insignificant pixels

LIS: List of insignificant sets

type A: set of coordinates of all descendants of the node

type B: set of coordinates of all descendants of the node, excluding offspring of the node

vqSPIHT

• to provide better quality for some regions or details

• implementation with matrix

vqSPIHT

: a percentage of the wanted final output file size

n: the number of significant bits in the coefficient table having the largest absolute value

ROI: Region of Interest

LUT: lookup table, to define whether a coefficient(i,j) contribute to any ROI or not

vqSPIHT

PSM: point significance matrix11:significant00: insignificant01:unknown

SPLM:sub-pyramid list matrixtype A: set of coordinates of all descendants

of the node type B: set of coordinates of all descendants

of the node, excluding offspring of the node

Test Results

• The comic test image of size 420*480 with 8bpp

Test Results

• The mammogram test image of size 2185*2925 with 12bpp

Test Results

• Practical memory requirements of the implementation for 12MB mammogram– SPIHT : 120MB– vqSPIHT: 50MB

Conclusions

• to encode different parts of an image with different bit-rates depending on their importance

• to simplify the implementation of SPIHT• to reduce working storage requirements• Combine the sending of significant

coefficients to sorting phase to avoid the problem of transmission interrupting