This paper is a revised version of an article by the same title and author which appeared in the April 1991 issue of Communications of the ACM. For the past few years, a joint ISO/CCITT committee known as JPEG (Joint Photographic Experts Group) has been working to establish the first international compression standard for continuous-tone still images, both grayscale and color. JPEG’s proposed standard aims to be generic, to support a wide variety of applications for continuous-tone images. To meet the differing needs of many applications, the JPEG standard includes two basic compression methods, each with various modes of operation. A DCT-based method is specified for “lossy’ ’ compression, and a predictive method for “lossless’ ’ compression. JPEG features a simple lossy technique known as the Baseline method, a subset of the other DCT-based modes of operation. The Baseline method has been by far the most widely implemented JPEG method to date, and is sufficient in its own right for a large number of applications. This article provides an overview of the JPEG standard, and focuses in detail on the Baseline method. 1

We present the results of applying lossless and lossy data compression to a three-dimensional object reconstruction and recognition technique based on phase-shift digital holography. We find that the best lossless (Lempel-Ziv, Lempel-Ziv-Welch, Huffman, Burrows-Wheeler) compression rates can be expected when the digital hologram is stored in an intermediate coding of separate data streams for real and imaginary components. The lossy techniques are based on subsampling, quantization, and discrete Fourier transformation. For various degrees of speckle reduction, we quantify the number of Fourier coefficients that can be removed from the hologram domain, and the lowest level of quantization achievable, without incurring significant loss in correlation performance or significant error in the reconstructed object domain.

The efficient digital representation of image and video signals has been subject of considerable research over the past 20 years. With the growing availability of digital transmission links, progress in signal processing, VU1 technology and image compression research, visual communications has become more feasible than ever. Digital video coding technology has developed into a mature jield and a diversity of products has been developed-targeted for a wide range of emerging applications, such as video on demand, digital TV/HDTV broadcasting, and multimedia image/video data-base services. With the increased commercial interest in video communications the need for international image and video coding standards arose. Standardization of video coding algorithms holds the promise of large markets for video communication equipment. Interoperability of implementations from different vendors enables the consumer to access video from a wider range of services and VU1 implementations of coding algorithms conforming to inter-national standards can be manufactured at considerably reduced costs. The purpose of this paper is to provide an overview of today’s image and video coding standards and their role in video communications. The different coding algorithms developed for each standard are reviewed and the commonalities between the standards are discussed.

visual communications

Introduction According to the well-known image transform coding paradigm, an appropriate transformation of the signal is carried out prior to the quantization stage to simplify the quantizer design. Given a certain transform (usually a local frequency transform), two intimately related problems must be solved. The first problem is to decide how to distribute quantization levels in the amplitude 1 Partially supported by CICYT projects TIC98-677-C02-02 and TIC 1FD97-279. 19 July 1999 range of each transform coefficient. The second problem is to determine the number of quantization levels that will be devoted to each coefficient. The first is usually known as 1D quantizer design and the second is often referred to as bit allocation. It is widely accepted that for image coding applications that are judged by a human observer, the properties and limitations of the human visual system (HVS) have to be taken into account

this paper are of 3D objects and are composed of complex-valued pixels, which means they cannot be processed directly with standard image compression tools. Very recently, modications to standard transform techniques, including the discrete cosine transform (DCT) [30] at the heart of baseline JPEG, for the compression of complex-valued digital holograms have been proposed [16]. The positive results from this work concur with previous studies with 2D images that show that careful manipulation of the quantised cosine coecients can improve image quality over baseline JPEG [31]

PIV and PTV analysis require the acquisition of series of images, representing the position of seeding particles at different times, resulting in a great amount of data to be stored on magnetic support, such as hard disk and magnetic tape. Typical dimensions of a PIV image are 1000 1000 pixels which results in 1 Mb per image (grey levels) to be stored. In this work the capabilities of different methods for data compression are evaluated and their effects on PIV and PTV analysis are compared.

Abstract—Due to the continuously decreasing cost of FPGAs, they have become a valid implementation platform for SOCs. Typically, a soft core processor implementation is used to execute the software parts of the SOC. As each system is individually designed for a particular application, the idea is natural to support compute intensive parts of the code through customized hardware acceleration. Two different architectural variants have been proposed for this purpose in SOCs: either as an instruction set extension with specialized pipeline implementation or as a peripheral component that is programmed through memory mapping. In this contribution we analyze the efficiency (speedup related to LUTs) of those two variants. I.