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In this paper, we present a novel audio coder using the discrete wavelet transform (DWT) and warped linear prediction (WLP). In contrast to conventional LP, WLP allows for the control of frequency resolution to closely match the response of the human auditory system. The structure of the system is similar to the transform coded excitation techniques used in wideband speech coding, where LP has been replaced with WLP, and the residual is analyzed by a wavelet filterbank designed to approximate the critical bands. The inherent shaping of the WLP synthesis filter, and a controlled bit allocation to the wavelet coefficients helps minimise the perceptually significant noise due to the quantization error in the residual. For monophonic signals sampled at 44.1 kHz, the coder achieves near transparent to transparent quality for a variety of speech and music signals at an average bitrate of about 64 kb/s. Tests also show that the coder (in its initial implementation) delivers superior quality to the MPEG layer III and comparable quality to the MPEG2-AAC codec when operating at the same bitrate. [ABSTRACT FROM AUTHOR]

The conventional two-dimensional wavelet transform used in existing image coders is usually performed through one-dimensional (1-D) filtering in the vertical and horizontal directions, which cannot efficiently represent edges and lines in images. The curved wavelet transform presented in this paper is carried out by applying 1-D filters along curves, rather than being restricted to vertical and horizontal straight lines. The curves are determined based on image content and are usually parallel to edges and lines in the image to be coded. The pixels along these curves can be well represented by a small number of wavelet coefficients. The curved wavelet transform is used to construct a new image coder. The code-stream syntax of the new coder is the same as that of JPEG2000, except that a new marker segment is added to the tile headers. Results of image coding and subjective quality assessment show that the new image coder performs better than, or as well as, JPEG2000. It is particularly efficient for images that contain sharp edges and can provide a PSNR gain of up to 1.67 dB for natural images compared with JPEG2000. [ABSTRACT FROM AUTHOR]

JPEG 2000 is a new image coding system that delivers superior compression performance and provides many advanced features in scalability, flexibility, and system functionalities that outperform all previous standards. It brings exciting possibilities to many imaging applications such as the Internet, wireless, security, and digital cinema. This paper gives an overview of this triumph in innovations and teamwork. It gives brief introductions to the four new parts that are under development by the JPEG committee. It attempts to summarize the lessons learned from the JPEG 2000 development experience and draw some conclusions on the success factors of this important standard. [ABSTRACT FROM AUTHOR]

One of the purposes of this article is to give a general audience sufficient background into the details and techniques of wavelet coding to better understand the JPEG 2000 standard. The focus is on the fundamental principles of wavelet coding and not the actual standard itself. Some of the confusing design choices made in wavelet coders are explained. There are two types of filter choices: orthogonal and biorthogonal. Orthogonal filters have the property that there are energy or norm preserving. Nevertheless, modern wavelet coders use biorthogonal filters which do not preserve energy. Reasons for these specific design choices are explained. Another purpose of this article is to compare and contrast “early” wavelet coding with “modern” wavelet coding. This article compares the techniques of the modern wavelet coders to the subband coding techniques so that the reader can appreciate how different modern wavelet coding is from early wavelet coding. It discusses basic properties of the wavelet transform which are pertinent to image compression. It builds on the background material in generic transform coding given, shows that boundary effects motivate the use of biorthogonal wavelets, and introduces the symmetric wavelet transform. Subband coding or “early” wavelet coding method is discussed followed by an explanation of the EZW coding algorithm. Other modern wavelet coders that extend the ideas found in the EZW algorithm are also described [ABSTRACT FROM PUBLISHER]