In 2005, the Consultative Committee for Space Data Systems (CCSDS) approved a new Recommendation (CCSDS 122.0-B-1) for Image Data Compression. Our group has designed a new file syntax for the Recommendation. The proposal
consists of adding embedded headers. Such modification provides scalability by quality, spatial location, resolution and
component. The main advantages of our proposal are: 1) the definition of multiple types of progression order, which enhances
abilities in transmission scenarios, and 2) the support for the extraction and decoding of specific windows of interest
without needing to decode the complete code-stream. In this paper we evaluate the performance of our proposal. First we
measure the impact of the embedded headers in the encoded stream. Second we compare the compression performance of
our technique to JPEG2000.
In 2005, the Consultative Committee for Space Data Systems (CCSDS) approved a new Recommendation (CCSDS 122.0-B-1) for Image Data Compression. This Recommendation defines a coding system for image-data compression applicable
to digital data from payload instruments, specifying both a file syntax to allow the transmission of the data in multiple
packets and techniques to control the compression ratio. In this paper we propose a new file syntax that provides scalability
by quality, spatial location, resolution and component. The main advantages of the proposed file syntax for the
Recommendation are: 1) the definition of multiple types of progression order, which enhances abilities in transmission
scenarios, and 2) the support for the extraction and decoding of specific windows of interest without needing to decode the
complete code-stream. This will enable the use of the Recommendation in interactive transmission scenarios.
In this paper we provide a study concerning the suitability of well-known image coding techniques originally devised for lossy compression of still natural images when applied to lossless compression of ultraspectral sounder data. We present here the experimental results of six wavelet-based widespread coding techniques, namely EZW, IC, SPIHT, JPEG2000, SPECK and CCSDS-IDC. Since the considered techniques are 2-dimensional (2D) in nature but the ultraspectral data are 3D, a pre-processing stage is applied to convert the two spatial dimensions into a single spatial dimension. All the wavelet-based techniques are competitive when compared either to the benchmark prediction-based methods for lossless compression, CALIC and JPEG-LS, or to two common compression utilities, GZIP and BZIP2. EZW, SPIHT, SPECK and CCSDS-IDC provide a very similar performance, while IC and JPEG2000 improve the compression factor when compared to the other wavelet-based methods. Nevertheless, they are not competitive when compared to a fast precomputed vector quantizer. The benefits of applying a pre-processing stage, the Bias Adjusted Reordering, prior to the coding process in order to further exploit the spectral and/or spatial correlation when 2D techniques are employed, are also presented.
High resolution images are becoming a natural source of data
for many different applications, for instance, remote sensing (RS)
and geographic information systems (GIS). High resolution is to be
understood as a combination of increasing spectral size, increasing spatial resolution per pixel, increasing bit depth resolution per pixel, and larger areas captured at once by the sensors. These images have, therefore, an increasing demand for both storage and transmission scenarios, so that there is a need for compression. Lossless coding, achieving at most 4:1 compression ratios, is seldom enough for applications without a great demand for visual detail. Lossy coding, that may well achieve over 200:1 compression ratios, may still be useful for some final user applications. We are interested in those lossy coding techniques that may fulfill the particular requirements of RS and GIS applications, i.e.: 1) availability of compression of both mono-band and multi-band (either multi or hyperspectral images); 2) high speed of data recovering (from the encoded bit stream) in all image regions, considering also embedded transmission; 3) zoom and lateral shift capability; 4) respect of no-data or meta-data regions, which should be maintained at any compression ratio; 5) in the case of lossy compression, lossless encoding of some physical parameters such as temperature, radiance, elevation, etc.; 6) to reach high compression ratios while maintaining the image quality. In this paper we review two such lossy coding techniques, namely the CCSDS-ILDC Recommendation and the recent JPEG2000 Standard.
High resolution images are nowadays a common source of data for many different applications; let us consider, for instance, hyperspectral images for remote sensing and geographic information systems. This kind of images allows for exhaustive analysis and provides good classification performance due to their high resolution (either bits per pixel, spatial, or spectral resolution). Nevertheless, this same high resolution, as well as their huge size, imposes a large demand of memory capability and channel bandwidth. To deal with this problem, lossy encoding of such images may be devised. Well known lossless and lossy image coding techniques have been used, but remote sensing and geographic information systems applications have some particular requirements that are not taken into account by the classical methods. There is therefore a need to investigate new approaches of image coding for these applications.
Remote Sensing and Geographic Information Systems applications are becoming more and more present in everyday life. These applications are based on the growing availability of natural, multi-spectral and hyper-spectral images, but these kind of images imposes a large demand of memory capability due to their large size and increasing resolution.
Well known lossless and lossy image coding techniques have been used to settle down this problem, but RS and GIS applications have some particular requirements that are not taken into account by the standard methods. There is therefore a need to investigate new approaches of image coding for RS and GIS applications.