Adaptive polyphase subband decomposition structures for image compression

TLDR

Subband decomposition is widely used for data coding, aiming to produce subsampled signals for different spectral regions, but it can introduce artifacts in images with spatially varying features such as text, subtitles, or sharp edges. The paper proposes adaptive filter banks with perfect reconstruction to mitigate these artifacts in such images. The decomposition employs filters that are either linear or nonlinear and adapt to the signal’s characteristics. The adaptive approach yields improved compression ratios, as demonstrated by simulation examples.

Abstract

Subband decomposition techniques have been extensively used for data coding and analysis. In most filter banks, the goal is to obtain subsampled signals corresponding to different spectral regions of the original data. However, this approach leads to various artifacts in images having spatially varying characteristics, such as images containing text, subtitles, or sharp edges. In this paper, adaptive filter banks with perfect reconstruction property are presented for such images. The filters of the decomposition structure which can be either linear or nonlinear vary according to the nature of the signal. This leads to improved image compression ratios. Simulation examples are presented.