John T. Reagan

Practical transform coding of multispectral imagery

In this paper we present a robust and implementable compression algorithm for multispectral imagery with a selectable quality level within the near-lossless to visually lossy range. The three-dimensional terrain-adaptive transform-based algorithm involves a one dimensional Karhunen-Loeve transform (KLT) followed by two-dimensional discrete cosine transform (DCT). The images are spectrally decorrelated via the KLT to produce the eigen images. The resulting spectrally decorrelated eigen images are then compressed using the JPEG algorithm. The key feature of this approach is that it incorporates the best methods available to fully exploit the spectral and spatial correlation in the data. The novelty of this technique lies in its unique capability to adaptively vary the characteristics of the spectral decorrelation transformation based upon variations in the local terrain. The spectral and spatial modularity of the algorithm architecture allows the JPEG to be replaced by a totally different coder (e.g., DPCM). However, the significant practical advantage of this approach is that it is leveraged on the standard and highly developed JPEG compression technology. The algorithm is conveniently parameterized to accommodate reconstructed image fidelities ranging from near- lossless at about 5:1 compression ratio (CR) to visually lossy beginning at around 40:1 CR.

Near lossless transform coding of multispectral images

The authors have extended the previously developed adaptive transform coding based multispectral algorithm for the lossless/virtually lossless case. Preliminary results are encouraging compared with traditional lossless implementation. The primary technical challenge is to compensate for the quantization errors introduced in processing the high dynamic range eigen images.

Terrain-adaptive transform coding of multispectral data

Presents a robust and implementable compression algorithm for multispectral imagery with selectable quality level within the near-lossless to visually lossy range. The three-dimensional terrain-adaptive transform-based algorithm involves a one dimensional Karhunen-Loeve transform (KLT) followed by two-dimensional discrete cosine transform (DCT). The images are spectrally decorrelated via the KLT to produce the eigen images. The resulting spectrally decorrelated eigen images are then compressed using the JPEG algorithm. The key feature of this approach is that it incorporates the best methods available to fully exploit the spectral and spatial correlation in the data. The novelty of this technique lies in its unique capability to adaptively vary the characteristics of the spectral decorrelation transformation based upon variations in the local terrain. The spectral and spatial modularity of the algorithm architecture allows the JPEG to be replaced by a totally different coder (e.g., DPCM). However. The significant practical advantage of this approach is that it is leveraged on the standard and highly developed JPEG compression technology. The algorithm is conveniently parameterized to accommodate reconstructed image fidelities ranging from near-lossless at about 5:1 compression ratio (CR) to visually lossy beginning at around 40:1 CR.<<ETX>>

Environmental data compression issues

The impact of lossy compression was considered for transmission of environmental image data. In the investigated implementation scenarios, it was found that compression at rates approaching 16:1 has minor impact on the exploitation and assessment of the ultimate derived automated cloud analysis, due to the robustness of this algorithm. Additional work is needed to evaluate the impact of compression on other products, such as sea surface temperature. The implementation included high dynamic range satellite imagery.

Spaced-based data compression issues

Some key issues related to space-based compression design are discussed. Various system considerations as well as potential compression options are also presented. A brief overview of a previously reported robust lossy transform coding algorithm is given followed by a study of its performance sensitivities. These sensitivities include (1) performance sensitivity to commonly observed anomalies in the data including band misalignment and dead/ saturated pixels, (2) impact of geometric distortion (processed versus unprocessed data) on compression performance, (3) performance sensitivity to different grouping of bands for spectral decorrelation, and (4) impact of compression on spectral fidelity. In addition, the impact of compression on the results of exploitation of environmental data including automated cloud study will be considered. It is shown that preprocessing to correct any geometric distortion noticeably improves the compression performance. Different groupings of bands also influence the performance. The loss of spectral fidelity, measured by the deviation from the original correlation coefficient matrix, is very insignificant regardless of the image and the coding bit rates. For the available bit rate, it is possible to trade off the compression-induced error between the spectral and spatial resolutions. In the implementation scenarios investigated, it was found that compression at rates approaching 16:1 has a minor impact on the exploitation and assessment of the ultimately derived automated cloud analysis. Additional work is needed to evaluate the impact of compression on other products, such as sea surface temperature. The results to date suggest that lossy compression may play a role in the efficient transmission of environmental information and in its subsequent exploitation.

A comparative study of the performance of time‐frequency representations for the analysis of echo returns.

In a scattering experiment, the contributions (pulses) associated with different dynamical scattering processes generally arrive at the field point at different times. The structure of these individual pulses is related to the characteristics of the underlying scattering dynamics (the ‘‘dynamical’’ bandwidth of the coupling, underlying wave dispersion, etc.). Thus the time‐frequency structure of the echo return is intimately connected not only with the frequency bandwidth and shape of the incident pulse, but also with the target response. Elements of the above have been discussed by previous authors [e.g., N. Yen et al., J. Acoust. Soc. Am. Suppl. 1 84, S185 (1988)]; in this paper, a comparative study is presented of the analysis capabilities of several time‐frequency analyses tools, including the Wigner distribution, Choi–Williams distribution, the Gabor transform, and continuous wavelet algorithms. The study is based on echo returns that have been synthesized from a numerical T‐matrix solution for a fin...

Improving the performance of the MPEG2 coder at high quality

The development of the MPEG video coding standard is an important step in the commercial development of digital media, but constrains further performance improvements that may be possible by innovative algorithmic methods. The approach described in this paper keeps the basic structure of the MPEG2 coder, but allows for pre- and post-processing of the video sequence. We have two broad options available. The first one is to perform image processing prior to encoding, so as to improve the compression-quality trade off in the subsequent MPEG2 coder. The second one is to use image processing on the decoded video to improve its quality. This second approach includes the widely recognized desire to reduce end of block impairments of DCT based coders. In this paper, we report some of our work on both pre- and post processing to improve performance. For this work, we focus on the use of data dependent inhomogeneous filtering that preserves the structure and thus quality of images, while reducing unwanted random noise.

Practical issues for transform coding of multispectral imagery

An overview of a robust and implementable compression algorithm previously developed for multispectral imagery is given. This three-dimensional terrain-adaptive transform-based algorithm involves a one dimensional Karhunen-Loeve (KLT) transform followed by two- dimensional discrete cosine transform. The images are spectrally decorrelated via the KLT to produce the eigen images. The resulting spectrally-decorrelated eigen images are then compressed using the JPEG algorithm. The key feature of this approach is that it incorporates the best methods available to fully exploit the spectral and spatial correlation in the data. The novelty of this technique lies in its unique capability to adaptively vary the characteristics of the spectral decorrelation transformation based upon variations in the local terrain. In addition several relevant practical issues are addressed. These issues include: (1) Handling the panchromatic sharpening band, (2) tradeoff between spectral and spatial fidelity, (3) different grouping of bands for spectral decorrelation, (4) impact of band misalignment on performance, (5) impact of dead and saturated pixels on performance, and (6) impact of preprocessing on performance.

Subpixel resolution for target tracking

Optical sensing mechanisms are designed to provide adequate resolution for the images of the intended objects. Often, the image of an object is so small that the resolution of the image falls beyond the resolution of the sensing device, and some method must be used to attain finer resolution. In these cases, a model-based approach, in which a parametric object model is assumed, can attain the desired sub-pixel resolution capabilities. In the model-based approach, the object model is convolved with the optical system and then matched against the limited number of available sensor samples. The unknown parameters of the object model are then determined by an appropriate estimation technique. This study will focus on estimating the two-dimensional location parameters (i.e. (x,y) location ) of a single point source from a limited number of sensor readings. We present a comparative study of three estimation techniques: maximum likelihood, centroiding, and conditional mean. The sub-pixel resolution capability of these techniques will be studied as a function of signal-to-noise ratio (SNR). The Cramer-Rao theoretical lower bound for unbiased estimators is derived for this problem, and it is shown that the maximum likelihood solution attains the Cramer-Rao bound for SNR’s considered. The merits and deficiencies of the three estimation techniques and their applicability to solving the problem for multiple point sources will also be addressed.

Transform coding of multispectral imagery

Future land remote sensing satellite systems will likely be constrained in terms of downlink communication bandwidth. To alleviate this limitation the data must be compressed. In this article we present a robust and implementable compression algorithm for multispectral imagery with a selectable quality level within the near-lossless to visually lossy range. The three-dimensional terrain-adaptive transform-based algorithm involves a one-dimensional Karhunen-Loeve transform (KLT) followed by two-dimensional discrete cosine transform (DCT). The images are spectrally decorrelated via the KLT to produce the eigen images. The resulting spectrally-decorrelated eigen images are then compressed using the JPEG algorithm. The key feature of this approach is that it incorporates the best methods available to fully exploit the spectral and spatial correlation in the data. The novelty of this technique lies in its unique capability to adaptively vary the characteristics of the spectral decorrelation transformation based upon variations in the local terrain. The spectral and spatial modularity of the algorithm architecture allows the JPEG to be replaced by a totally different coder (e.g., DPCM). However, the significant practical advantage of this approach is that it is leveraged on the standard and highly developed JPEG compression technology. The algorithm is conveniently parameterized to accommodate reconstructed image fidelities ranging from near- lossless at about 5:1 compression ratio (CR) to visually lossy beginning at around 40:1 CR.