Embedded image processing systems have to face heavier and heavier constraints in order to cope with the growing complexity of the algorithm and the increasing flexibility required by applications, while fulfilling more and more demanding implementation constraints. With a variety of target application profiles that include digital photography and remote sensing, the JPEG2000 standard is a typical example where complex and scalable techniques have to be implemented into highly integrated platforms. While the electronic system design community has pushed design-by-reuse as a key to manage complexity, providing a reusable hardware arhtiecture for JPEG2000 while preserving the high flexibility required by the wide application space is a major challenge. In this paper, we propose to address this issue by relying on a new class of synthesis tools: high-level synthesis (HLS) tools. HLS allows to specify hardware at a high abstraction level, where a variety of functional and architectural properties can be made customizable, and provides an automatic, constraint-driven architectural refinement flow that allows to generate a detailed register-transfer-level architecture from a behavioral (algorithmic-like) description of a component's behavior. Using a commercial HLS tool, we were able to generate a variety of JPEG2000-compliant discrete wavelet transform architectures, with varying hardware complexity and computation speed, from a single behavioral-level VHDL specification.
Future high resolution instruments planned by CNES for space remote sensing missions will lead to higher bit rates because of the increase in both resolution and number of bits per pixel, not compensated by the reduced swath. Data compression is then needed, with compression ratio goals always higher and with artifacts remaining unnoticeable. Up to now studied algorithms are based on intra-band coding and utilize the intra-image or spatial correlation. The spaceborne earth observation instruments have however several spectral channels (one panchromatic band and at least 3 spectral bands) and since such algorithms process independently each channel, the inter-image or spectral correlation is ignored. For optimum compression performance, multispectral algorithms have to be studied in order to exploit both spectral and spatial correlation. This paper proposes a low complexity and flexible fixed data rate compression algorithm for multispectral imagery.
Future high resolution instruments planned by CNES to succeed SPOT5 will lead to higher bit rates because of the increase in both resolution and number of bits per pixel, not compensated by the reduced swatch. Data compression is then needed, with compression ratio goals higher than the 2.81 SPOT5 value obtained with a JPEG like algorithm. Compression ratio should rise typically to 4 - 6 values, with artifacts remaining unnoticeable: SPOT5 algorithm performances have clearly to be outdone. On another hand, in the framework of optimized and low cost instruments, noise level will increase. Furthermore, the Modulation Transfer Function (MTF) and the sampling grid will be fitted together, to -- at least roughly -- satisfy Shannon requirements. As with the Supermode sampling scheme of the SPOT5 Panchromatic band, the images will have to be restored (deconvolution and denoising) and that renders the compression impact assessment much more complex. This paper is a synthesis of numerous studies evaluating several data compression algorithms, some of them supposing that the adaptation between sampling grid and MTF is obtained by the quincunx Supermode scheme. The following points are analyzed: compression decorrelator (DCT, LOT, wavelet, lifting), comparison with JPEG2000 for images acquired on a square grid, compression fitting to the quincunx sampling and on board restoration (before compression) versus on ground restoration. For each of them, we describe the proposed solutions, underlining the associated complexity and comparing them from a quantitative and qualitative point of view, giving the results of experts analyses.
On board Image compression is a very powerful tool to optimize the onboard resources needed to store and transmit image data to ground. This is due to the steady performance improvements of the compression algorithms and to the availability, for spaceborne applications, of highly integrated circuits (ASIC technology) that made it possible to implement very sophisticated real-time schemes. We propose in this paper a survey of on on-board image compression with emphasis on some compression architectures and present the future prospects.
Conference Committee Involvement (9)
Applications of Digital Image Processing XXXIV
22 August 2011 | San Diego, California, United States
Applications of Digital Image Processing XXXIII
2 August 2010 | San Diego, California, United States
Applications of Digital Image Processing XXXII
3 August 2009 | San Diego, California, United States
Applications of Digital Image Processing XXXI
11 August 2008 | San Diego, California, United States
Applications of Digital Image Processing XXX
28 August 2007 | San Diego, California, United States
Applications of Digital Image Processing XXIX
15 August 2006 | San Diego, California, United States
Applications of Digital Image Processing XXVIII
2 August 2005 | San Diego, California, United States
Applications of Digital Image Processing XXVII
2 August 2004 | Denver, Colorado, United States
Applications of Digital Image Processing XXVI
5 August 2003 | San Diego, California, United States
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