On the influence of clipping in lossless predictive and wavelet coding of noisy images

TL;DR

This paper examines how clipping affects lossless image compression of noisy images, comparing entropy, predictive, and wavelet-based methods through theoretical and simulated results.

Contribution

It provides a comparative analysis of three lossless coding approaches and highlights the impact of noise and clipping on compression efficiency.

Findings

Clipping increases the number of bits needed for lossless compression.

Without clipping, predictive and wavelet methods outperform entropy coding.

For very noisy signals, direct entropy coding without preprocessing is more effective.

Abstract

Especially in lossless image coding the obtainable compression ratio strongly depends on the amount of noise included in the data as all noise has to be coded, too. Different approaches exist for lossless image coding. We analyze the compression performance of three kinds of approaches, namely direct entropy, predictive and wavelet-based coding. The results from our theoretical model are compared to simulated results from standard algorithms that base on the three approaches. As long as no clipping occurs with increasing noise more bits are needed for lossless compression. We will show that for very noisy signals it is more advantageous to directly use an entropy coder without advanced preprocessing steps.