Subband coding of images

TLDR

Subband coding has become popular for source encoding of speech. The paper extends subband coding to efficient image source coding. The authors design a 2‑D quadrature‑mirror‑filter subband coder, optimally allocate bits via DPCM and a local‑variance mask, use Laplacian‑matched quantization, and benchmark its SNR against adaptive DCT, VQ, and DVQ on 256×256 images. The adaptive subband coder achieves the highest SNR among compared methods.

Abstract

Subband coding has become quite popular for the source encoding of speech. This paper presents a simple yet efficient extension of this concept to the source coding of images. We specify the constraints for a set of two-dimensional quadrature mirror filters (QMF's) for a particular frequency-domain partition, and show that these constraints are satisfied by a separable combination of one-dimensional QMF's. Bits are then optimally allocated among the subbands to minimize the mean-squared error for DPCM coding of the subbands. Also, an adaptive technique is developed to allocate the bits within each subband by means of a local variance mask. Optimum quantization is employed with quantizers matched to the Laplacian distribution. Subband coded images are presented along with their signal-to-noise ratios (SNR's). The SNR performance of the subband coder is compared to that of the adaptive discrete cosine transform (DCT), vector quantization, and differential vector quantization for bit rates of 0.67, 1.0, and 2.0 bits per pixel for 256 × 256 monochrome images. The adaptive subband coder has the best SNR performance.