Preserving step edges in low bit rate progressive image compression

Abstract

With the growing importance of low-bandwidth applications, such as wireless access to the Internet, images are often sent or received at low bit rates. At these bit rates, they suffer from significant distortion and artifacts, making it difficult for those viewing the images to understand them. We present two progressive compression algorithms that focus on preserving the clarity of important image features, such as edges, at compression ratios of 80:1 and more. Both algorithms capture and encode the locations of important edges in the images. The first algorithm then transmits a standard SPIHT (set partitioning in hierarchical trees) bit stream, and at the decoder applies a nonlinear edge-enhancement procedure to improve the clarity of the encoded edges. The second approach uses a modified wavelet transform to "remove" the edges, and encodes the remaining texture information using SPIHT. With both approaches, features in the images that may be important for recognition are well preserved, even at low bit rates.

New algorithms for preserving edges in low‐bit‐rate wavelet‐based image compression

“…An algorithm named EEIC (edge‐enhancing image coder) has been presented in Ref. [4]. By transmitting the edge information extracted at the encoder, the algorithm can enhance the edge with a nonlinear procedure at the decoder.…”

“…At the same bit rates, the postprocessing algorithm achieves the edge‐enhancement procedure at lower bit rates than EEIC. With the usual algorithms [4,5], the PSNR is always below the compressed image from the SPIHT algorithm. In the Section 4, we compare the proposed algorithm with SPIHT and present the best PSNR values at low bit rates.…”

“…At low bit rates, wavelet‐based image coding performs significantly better than the traditional block‐based algorithms in terms of image quality. One way to reduce artifacts around edges might be by transmitting the primary edge information first[4]; along this idea, error concealment in the form of postprocessing can improve the quality of the compressed image. Reduction of wavelet‐based artifacts and thus improvement of the overall perceptual quality of the image also has been mentioned in [5].…”

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

“…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.…”