Bernard V. Brower

Low-bit-rate image compression evaluations

In order to assist the National Imagery Transmission Format Standard (NITFS) Technical Board (NTB) in selecting new BWC algorithm(s), evaluations of candidate image compression algorithms were performed on the basis of objective and subjective image quality performance, bit rate control, susceptibility to channel errors, and complexity of implementation. Based on these evaluations, which were conducted under the guidance of the NTB, it was concluded that the ISO/JPEG DCT compression algorithm was the most suitable for the NITFS purpose even though two proprietary sub-band coding techniques generally performed better in subjective image quality. Moreover, it was decided that three algorithms would be further evaluated at very low bit rates where the ISO/JPEG DCT does not perform optimally.

JPEG-2000 compression using 3D wavelets and KLT with application to HYDICE data

JPEG-2000 is the new image compression standard currently under development by ISO/IEC. Part I of this standard provides a “baseline” compression technology appropriate for grayscale and color imagery. Part II of the standard will provide extensions that allow for more advanced coding options, including the compression of multiple component imagery. Several different multiple component compression techniques are currently being investigated for inclusion in the JPEG-2000 standard. In this paper we apply some of these techniques toward the compression of HYDICE data. Two decorrelation techniques, 3D wavelet and Karhunen-Loeve Transform (KLT), were used along with two quantization techniques, scalar and trellis-coded (TCQ), to encode two HYDICE scenes at five different bit rates (4.0, 2.0, 1.0, 0.5, 0.25 bits/pixel/band). The chosen decorrelation and quantization techniques span the range from the simplest to the most complex multiple component compression systems being considered for inclusion in JPEG-2000. This paper reports root-mean-square-error (RMSE) and peak signal-to-noise ratio (PSNR) metrics for the compressed data. A companion paper [1] that follows reports on the effects of these compression techniques on exploitation of the HYDICE scenes.

An enhanced space-qualified downlink image compression ASIC for commercial remote sensing applications

Eastman Kodak Company developed a rate-controlled adaptive Differential Pulse Code Modulation (DPCM) image compression algorithm for commercial remote sensing applications. This algorithm is currently being used in a space-qualified ASIC in the Space Imaging Incorporated IKONOS satellite and the soon- to-be-launched EarthWatch QuickBird satellite. This ASIC compresses the raw imagery data (before calibration) at a speed just under 4 Megapixels per second. Kodak has redesigned this ASIC to increase the functionality and throughput while maintaining the power and area. With advancements in ASIC design, the compression algorithm, and fabrication techniques, the new compression ASIC has achieved the operating rate of 22 Megapixels per second. A third option mode has also been added to increase the capability of the ASIC to achieve lossless compression ratios of 2:1 to lossy compression ratios of 5:1. This new ASIC is intended to meet the future commercial remote sensing requirements for increased resolution and greater area coverage.

Hyperspectral compression using spectral signature matching with error encoding

Hyperspectral image data present increasing challenges to current transmission bandwidth and storage capabilities. The large amounts of spectrally redundant information present in these data make hyperspectral compression techniques extremely attractive. This paper presents a hyperspectral compression algorithm which was designed to maintain the spectral accuracy needed for standard hyperspectral analytical techniques. Spectral accuracy is maintained through an approach that extracts and separately codes the hyperspectral signatures present in each pixel.

Data characterization for hyperspectral image compression

By their very nature, hyperspectral imagers collect much more data per pixel than more traditional imaging systems. With bandwidth limitations on the communications channels and storage space, intelligent system design, band selection, and/or data compression will be very important. In two recent government-funded studies (completed in Dec. 1996), Kodak developed two preliminary compression options for hyperspectral imaging. As part of these studies, the band-to- band data correlation structures for both AVIRIS and HYDICE hyperspectral imaging systems were evaluated. Some surprising results were noted that have important implications to system designers.

Spectrally and spatially adaptive hyperspectral data compression

A hyperspectral data compression algorithm is presented that utilizes a modular approach of an adaptive spectral transform to decorrelate the spectral bands, which are then adaptively spatially compressed. The adaptivity in the spectral transform is dependent upon the spectral characteristics (spectral correlation) and the importance of the band. Correlation is very high between most bands of hyperspectral data, which suggests a large amount of redundant information. The bands with less correlation indicate either a significant amount of non-redundant information or poor signal-to-noise characteristics. These spectral characteristics have been shown to be very dependent on the imaging system and atmospheric conditions of the hyperspectral image. The importance of any given band is dependent upon the users needs, exploitation task and the imaging system. This leads to a spatial compression technique that is selected dependent upon the expected spatial correlation.

Multiple component compression within JPEG 2000 as compared to other techniques

The efficient compression of greater than three-component imagery has not been allowed within current image compression standards. The advanced JPEG 2000 image compression standard will have provisions for multiple component imagery that will enable decorrelation in the component direction. The JPEG 2000 standard has been defined in a flexible manner, which allows for the use of multiple transform techniques to take advantage of the correlation between components. These techniques will allow the user to make the trade between complexity and compression efficiency. This paper compares the compression efficiency of three techniques within the JPEG 2000 standard against other standard compression techniques. The results show that the JPEG 2000 algorithm will significantly increase the compression efficiency of multiple-component imagery.

Multicomponent compression in JPEG 2000 Part II

JPEG2000 Part I provides a host of compression options and data ordering choices which enable powerful applications and create tremendous flexibility in the handling of still images. Part I, however, is restricted to handle multiple component images (with the exception of three-component images) a single component at a time. In general, Part I allows no exploitation of inter-component correlation that may exist. Part II introduces a robust multiple component transform capability which is applied prior to the Part I spatial wavelet decomposition and compression. This paper describes some of the multiple component transform capabilities in JPEG2000 Part II, including prediction, traditional decorrelation, wavelet transformations, and reversible integer transformations.

Hyperspectral lossless compression

Hyperspectral image data presents challenges to current transmission bandwidth and storage capabilities. To overcome these challenges and to retain the radiometric accuracy of the data, there is a need for good hyperspectral lossless compression. The current state-of-the-art lossless compression algorithm is JPEG-LS, which uses a 2-D edge-detecting predictor. Hyperspectral systems sample the electromagnetic spectrum very finely, which results in increased spectral correlation. A predictor that takes into account previous band information can obtain substantial gains in compression ratio. This paper discusses a number of different predictors that take advantage of the significant band-to-band (spectral) correlation within the hyperspectral imagery. A sample set of HYDICE, AVIRIS, and SEBASS imagery was used to evaluate the different predictors. While the JPEG-LS algorithm achieved just greater than 2:1 on most imagery, some of the 3-D prediction techniques achieved greater than 3:1 compression ratio. The characteristics of these test images and results from different predictors are presented in this paper.

Real-time access of large volume imagery through low-bandwidth links

Providing current, time-sensitive imagery and geospatial information to deployed tactical military forces or first responders continues to be a challenge. This challenge is compounded through rapid increases in sensor collection volumes, both with larger arrays and higher temporal capture rates. Focusing on the needs of these military forces and first responders, ITT developed a system called AGILE (Advanced Geospatial Imagery Library Enterprise) Access as an innovative approach based on standard off-the-shelf techniques to solving this problem. The AGILE Access system is based on commercial software called Image Access Solutions (IAS) and incorporates standard JPEG 2000 processing. Our solution system is implemented in an accredited, deployable form, incorporating a suite of components, including an image database, a web-based search and discovery tool, and several software tools that act in concert to process, store, and disseminate imagery from airborne systems and commercial satellites. Currently, this solution is operational within the U.S. Government tactical infrastructure and supports disadvantaged imagery users in the field. This paper presents the features and benefits of this system to disadvantaged users as demonstrated in real-world operational environments.