Feature-preserving image coding for very low bit rates

Abstract

Many progressive wavelet-based image coders are designed for good performance on natural images. They attempt to achieve the greatest reduction in mean squared error (MSE) with each bit sent, an approach that is most effective when the image is composed chiefly of low-frequency content. Many images, however, include sharp-edged objects, text characters or graphics that are not well handled by standard wavelet-based methods. These features, which may contain information important for recognition, become distorted and obscured when highly compressed by standard wavelet-based methods. In this paper, we present a new progressive image coder that treats an image as being composed of three types of information: edges, texture, and edge-associated detail. The locations of important edges are encoded using line graphic techniques. Texture is encoded using a wavelet-based zerotree approach. Detail near edgesthat cannot be efficiently encoded as textureis encoded separately with a bitplane coding technique. With this approach, features in the image that may be important for recognition are well preserved, even at low bit rates.

A New Texture Preserving Lossy Image Coder: A Comparison Using the Discrete Wavelet Transform and the Wavelet Based Contourlet Transform

“…In order to design a feature preserving lossy image coder it is important first, identify the features of the image to preserve and second, spend more bits to represent the important information and sacrifice fidelity or quality in other image regions [3].…”

“…The work presented in [3] shows a preserving system in which the features of the images (edges, textures and edge associated details) are extracted, separated and coded with different methods. The important edges are coded using line graphic techniques, textures using a wavelet based zerotree approach, and edge details associated are bitplane coded.…”

“…In recent years, there has been considerable effort into designing image coders in which the main goal is to obtain good quality decompressed images even at very low bit rates [1], [2], [3], [4]. Due to the great use of digital information, image compression becomes imperative in different areas such as image storage, transmission, processing, at these areas the representation of the information needs to be efficient.…”

Edge Preserving Lossy Image Compression with Wavelets and Contourlets

“…The combination of lossy image compression with feature preserving is a very interesting topic and becomes imperative in areas such as medical and textile in which there are laws and restrictions about the use of the original images [2]. In the literature there are a few works that take advantage of this.…”

On Bivariate Smoothness Spaces Associated with Nonlinear Approximation

“…Our efforts to proceed in this direction rely on recent attempts (e.g., [3], [4], [8], [15], [18], [26], [27], [29]) to find compact representations for multivariate signals by combining traditional coding methods such as wavelet compression with computation of lower-dimensional structure, for example segmentation in the case of images. Indeed, one of the goals of this work is to try and understand on which 'class' of functions these approaches outperform wavelets.…”