Max H. M. Costa

Automatic registration of satellite images

Image registration is one of the basic image processing operations in remote sensing. With the increase in the number of images collected every day from different sensors, automated registration of multi-sensor/multi-spectral images has become an important issue. A wide range of registration techniques has been developed for many different types of applications and data. Given the diversity of the data, it is unlikely that a single registration scheme will work satisfactorily for all different applications. A possible solution is to integrate multiple registration algorithms into a rule-based artificial intelligence system, so that appropriate methods for any given set of multisensor data can be automatically selected. The objective of this paper is to present an automatic registration algorithm which has been developed at INPE. It uses a multiresolution analysis procedure based upon the wavelet transform. The procedure is completely automatic and relies on the grey level information content of the images and their local wavelet transform modulus maxima. The algorithm was tested on SPOT and TM images from forest, urban and agricultural areas. In all cases we obtained very encouraging results.

Efficient run-length encoding of binary sources with unknown statistics

This paper describes an efficient run-length encoding of binary sources with unknown statistics. Binary entropy coders are used in many multimedia codec standards, which uses adaptive Golomb-Rice coders. Using a maximum-likelihood approach, an excess rate for the Golomb-like coder when compared to an adaptive Rice coder is up to 4.2% for binary sources with unknown statistics with respect to the source entropy.

Performance evaluation of data compression systems applied to satellite imagery

Onboard image compression systems reduce the data storage and downlink bandwidth requirements in space missions. This paper presents an overview and evaluation of some compression algorithms suitable for remote sensing applications. Prediction-based compression systems, such as DPCM and JPEG-LS, and transform-based compression systems, such as CCSDS-IDC and JPEGXR, were tested over twenty multispectral (5-band) images from CCD optical sensor of the CBERS-2B satellite. Performance evaluation of these algorithms was conducted using both quantitative rate-distortion measurements and subjective image quality analysis. The PSNR, MSSIM, and compression ratio results plotted in charts and the SSIM maps are used for comparison of quantitative performance. Broadly speaking, the lossless JPEG-LS outperforms other lossless compression schemes, and, for lossy compression, JPEG-XR can provide lower bit rate and better tradeoff between compression ratio and image quality.

Noisebergs in Z Gaussian interference channels

We propose an efficient scheme to transmit information over a Z Gaussian interference channel. The scheme uses the concept of water filling to provide optimal power sharing among orthogonal dimensions. The model under investigation is an one-sided Gaussian interference channel with interference parameter a in the range (0,1), which can be recast as a degraded Gaussian interference channel. In the proposed solution, the notion of noisebergs (noise icebergs) arises, where noise power floats above signal power in a water filling representation of the problem, providing an improved allocation of power and degrees of freedom. The solution is best characterized by a graphical representation in the frequency domain.

A tree construction method of nested cyclic codes

This paper presents a tree-based algebraic construction of nested cyclic codes through a tree construction method. These codes can be used to encode different data packets, producing codewords that are added for transmission. Both encoding and decoding are performed by polynomial operations with no need of side information, so the proposed scheme may be useful in applications that require low computational complexity. In cases where the number of information sources is large, it is convenient to design the system in a systematic manner. Hence, the tree construction method may contribute to simplify the code design.

From Almost Gaussian to Gaussian

We consider lower and upper bounds on the difference of differential entropies of a Gaussian random vector and an approximately Gaussian random vector after they are “smoothed” by an arbitrarily distributed random vector of finite power. These bounds are important to establish the optimality of the corner points in the capacity region of Gaussian interference channels. A problematic issue in a previous attempt to establish these bounds was detected in 2004 and the mentioned corner points have since been dubbed “the missing corner points”. The importance of the given bounds comes from the fact that they induce Fano-type inequalities for the Gaussian interference channel. Usual Fano inequalities are based on a communication requirement. In this case, the new inequalities are derived from a non-disturbance constraint. The upper bound on the difference of differential entropies is established by the data processing inequality (DPI). For the lower bound, we do not have a complete proof, but we present an argument based on continuity and the DPI.

Gaussian Z-interference channel: Around the corner

We investigate the vicinity of the newly established corner point for the capacity region of the Gaussian Z-interference channel. We determine the expression for the slope of the Han and Kobayashi region with Gaussian signaling just around the corner.

Almost there corner points of Gaussian interference channels

Almost-Gaussian (AG) and almost-lossless (AL) properties are used to derive almost trivial proofs of almost-achievable corner points of the capacity region of Gaussian interference channels. For the missing corner point, the proof is almost complete.

On the Adaptive Coefficient Scanning of JPEG XR/HD Photo

We explore several local and global strategies for adaptive scan ordering of transform coefficients in JPEG XR/HD Photo. This codec applies a global adaptive scan-order heuristic with respect to an effective localized predictor. The global ordering heuristic, although simple, performs as well as localized techniques that are computationally significantly more complex. We conclude that effective localized prediction not only minimizes but also essentially randomizes coefficient residuals, so that a global statistic is sufficient to deliver near-optimal compression performance.

On some almost properties

Previous works have shown that regular distributions with differential entropy or mean-squared error behavior close to that of the Gaussian are also close to the Gaussian with respect to some distances like Kolmogorov-Smirnov or Wasserstein distances, or vice versa. In keeping with these results, we show that under the assumption of a functional dependence on the Gaussian, any regular distribution that is almost Gaussian in differential entropy has a mean-squared error behavior of an almost linear estimator. A partial converse result is established under the addition of an arbitrary independent quantity: a small mean-squared error yields a small entropy difference. The proofs use basic properties of Shannons information measures and can be employed in an alternative solution to the missing corner point problem of Gaussian interference channels.