Michael J. Gormish

An overview of JPEG-2000

JPEG-2000 is an emerging standard for still image compression. This paper provides a brief history of the JPEG-2000 standardization process, an overview of the standard, and some description of the capabilities provided by the standard. Part I of the JPEG-2000 standard specifies the minimum compliant decoder, while Part II describes optional, value-added extensions. Although the standard specifies only the decoder and bitstream syntax, in this paper we describe JPEG-2000 from the point of view of encoding. We take this approach, as we believe it is more amenable to a compact description more easily understood by most readers.

JPEG 2000 for efficent imaging in a client/server environment

The JPEG 2000 image compression system offers significant opportunity to improve imaging over the Internet. The JPEG 2000 standard is ideally suited to the client/server architecture of the web. With only one compressed version stored, a server can transmit an image with the resolution, quality, size, and region custom specified by an individual client. It can also serve an interactive zoom and pan client application. All of these can be achieved without server side decoding while using only minimal server computation, storage, and bandwidth. This paper discusses some of the system issues involved in Internet imaging with JPEG 2000. The choices of the client, passing of control information, and the methods a server could use to serve the client requests are presented. These issues include use of JPEG 2000 encoding and the decoding options in the standard. Also, covered are some proposed techniques that are outside the existing standards.

Decoding compression with reversible embedded wavelets (CREW) codestreams

As the applications of digital imagery expand in resolution and pixel fidelity there is a greater need for more efficient compression and extraction of images and subimages. No longer is it sufficient to compress and decompress an image for a specific target device. The ability to handle many types of image data, extract images at different resolutions and quality, lossless and lossy, zoom and pan, and extract regions-of-interest are the new measures of image compression system performance. Compression with reversible embedded wavelets (CREW) is a high-quality image compression system that is progressive from high compression to lossless, and pyramidal in resolution. CREW supports regions-of-interest, and multiple image types, such as bi-level and continuous-tone. This paper describes the CREW system and format, shows how the correct data can be quickly extracted from a CREW file to support a variety of target devices, describes the mechanisms needed for panning, zooming, and fixed-size compression, and explains the superior performance on bi-level and graphic images.

JPEG 2000: overview, architecture, and applications

JPEG 2000 will soon be an international standard for still image compression. This paper describes that standard at a high level, indicates the component pieces which empower the standard, and gives example applications which highlight differences between JPEG 2000 and prior image compression standards.

Next Generation Image Compression And Manipulation Using CREW

As the applications of digital imagery expand in resolution and pixel fidelity there is a greater need for more efficient compression and extraction of images and sub-images. No longer is it sufficient to compress and decompress an image for a specific target device. The ability to handle many types of image data, extract images at different resolutions and quality, zoom and pan, and to extract regions-of-interest are the new measures of image compression systems. CREW (compression with reversible embedded wavelets) is a high quality image compression system that is progressive from high compression to lossless, pyramidal, and supports regions-of-interest and multiple image types. This paper describes the CREW system and format, shows how the correct data can be quickly extracted from a CREW file to support a variety of target devices, describes the mechanisms needed for panning, zooming, and fixed-size compression, and explains the superior performance on bi-level and graphic images.As the applications of digital imagery expand in resolution and pixel fidelity there is a greater need for more efficient compression and extraction of images and sub-images. No longer is it sufficient to compress and decompress an image for a specific target device. The ability to handle many types of image data, extract images at different resolutions and quality, zoom and pan, and to extract regions-of-interest are the new measures of image compression systems. CREW (compression with reversible embedded wavelets) is a high quality image compression system that is progressive from high compression to lossless, pyramidal, and supports regions-of-interest and multiple image types. This paper describes the CREW system and format, shows how the correct data can be quickly extracted from a CREW file to support a variety of target devices, describes the mechanisms needed for panning, zooming, and fixed-size compression, and explains the superior performance on bi-level and graphic images.

Very high speed entropy coding

Efforts to build high-speed hardware for many different entropy coders are limited by fundamental feedback loops. A method that allows for parallel compression in hardware is described. This parallelism results in extremely high rates, 100 million symbols/second or higher. The system is generalizable to any lossless or lossy system with deterministic decompression. Prototype hardware that divides the data into multiple streams that feed parallel coders is presented. The problem of efficient transmission of multiple streams of variable-length coded data is solved by a unique coded data interleave method.<<ETX>>

Mobile phones and information capture in the workplace

Smartphones (mobile phones with downloadable applications) are being used for far more than making calls and reading email. We investigated the use of phones for information capture for work purposes through interviews, multiple free response surveys, and two multiple choice surveys. While we expected and found taking pictures to be useful for work, we were surprised at the extent of audio, video, and note taking done on the phone, and the impact on other devices. Our work also suggests future mobile information capture for work will increase more due to cultural changes than technological improvements.

A new wavelet-based approach to sharpening and smoothing of images in Besov spaces with applications to deblurring

The problem of image enhancement arises in many applications such as scanners, copiers and digital cameras. Enhancement often includes a denoising and a deblurring or sharpening step. Similar to image compression, state-of-the-art denoising techniques use wavelet bases instead of Fourier bases since wavelet domain processing provides local adaptation in smooth and non-smooth parts due to the theoretical link between wavelets and smoothness spaces. In this paper the same smoothness spaces are used to propose a way of performing sharpening and smoothing of signals with wavelets (WSS) in Besov spaces. As an application the completely wavelet-based enhancement of a scanned document is discussed.

Computation-rate-distortion in transform coders for image compression

We consider the computational complexity of block transform coding and tradeoffs among computation, bit rate, and distortion. In particular, we illustrate a method of coding that allows decompression time to be traded with bit rate under a fixed quality criteria, or allows quality to be traded for speed with a fixed average bit rate. We provide a brief analysis of the entropy coded infinite uniform quantizer that leads to a simple bit allocation for transform coefficients. Finally, we consider the computational cost of transform coding for both the discrete cosine transform (DCT) and the Karhunen-Loeve transform (KLT). In general, a computation-rate- distortion surface can be used to select the appropriate size transform and the quantization matrix for a given bandwidth/CPU channel.

Discrete minimum entropy quantization

We present a simple lower bound on the entropy of a quantized signal and a method for determining the minimum entropy quantization, subject to a maximum distortion, for a discrete memoryless random process. This quantization allows more efficient use of variable-order models for compression of images and sound.