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Lensless Fluorescent Microscopy on a Chip

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

An analysis of some common scanning techniques for lossless image coding.

N Memon¹, D L Neuhoff, S Shende

¹Dept. of Comput. Sci., Polytech. Univ., Brooklyn, NY 11201, USA. memon@poly.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 12, 2008

Summary

Pixel scan order significantly impacts lossless image compression. The study found hierarchical scans offer the best performance, outperforming Hilbert and raster scans for predictive image coding.

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Area of Science:

Digital image processing
Information theory
Computer vision

Background:

Traditional image coding relies on raster scan, but alternative scans like Hilbert offer better locality preservation.
A quantitative understanding of how pixel scan order affects compression performance has been limited.

Purpose of the Study:

To develop a quantitative method for analyzing pixel scan order effects in context-based predictive lossless image compression.
To compare the performance of raster, Hilbert, random, and hierarchical scan orders.

Main Methods:

Developed formulas to estimate encoding rate based on context frequencies and conditional differential entropies for a quantized-Gaussian image model.
Analyzed context frequencies and entropies for different scan orders, assuming an isotropic image model and adjacent pixel contexts.

Main Results:

Raster scan outperformed the Hilbert scan, contrary to expectations based on locality.
Hierarchical scan demonstrated superior performance, even with nonadjacent contexts.
Random scan yielded the worst compression results among the evaluated methods.

Conclusions:

Pixel scan order is a critical factor in lossless image compression efficiency.
Hierarchical scan orders present a promising avenue for improved predictive image coding performance.
The findings have implications for both lossless and lossy image compression techniques.