A Novel Rate Control Algorithm for Onboard Predictive Coding of Multispectral and Hyperspectral Images

Predictive coding is attractive for compression onboard of spacecrafts thanks to its low computational complexity, modest memory requirements and the ability to accurately control quality on a pixel-by-pixel basis. Traditionally, predictive compression focused on the lossless and near-lossless modes of operation where the maximum error can be bounded but the rate of the compressed image is variable. Rate control is considered a challenging problem for predictive encoders due to the dependencies between quantization and prediction in the feedback loop, and the lack of a signal representation that packs the signal's energy into few coefficients. In this paper, we show that it is possible to design a rate control scheme intended for onboard implementation. In particular, we propose a general framework to select quantizers in each spatial and spectral region of an image so as to achieve the desired target rate while minimizing distortion. The rate control algorithm allows to achieve lossy, near-lossless compression, and any in-between type of compression, e.g., lossy compression with a near-lossless constraint. While this framework is independent of the specific predictor used, in order to show its performance, in this paper we tailor it to the predictor adopted by the CCSDS-123 lossless compression standard, obtaining an extension that allows to perform lossless, near-lossless and lossy compression in a single package. We show that the rate controller has excellent performance in terms of accuracy in the output rate, rate-distortion characteristics and is extremely competitive with respect to state-of-the-art transform coding

Performance comparison of blind and non-blind channel equalizers using artificial neural networks

In digital communication systems, multipath propagation induces Inter Symbol Interference (ISI). To reduce the effect of ISI different channel equalization algorithms are used. Complex equalization algorithms allow for achieving the best performance but they do not meet the requirements for implementation of real-time detection at low complexity, thus limiting their application. In this paper, we present different blind and non-blind equalization structures based on Artificial Neural Networks (ANNs) and, also, we analyze their complexity versus performance. Since the activation function at the output layer depends on the cost function with respect to the input, in the present work we use mean squared error as loss function for the output layer. The simulated network is based on multilayer feedforward perceptron ANN, which is trained by utilizing the error back-propagation algorithm. The weights of the network are updated in accordance with training of the network to improve the convergence speed. Simulation results demonstrate that the implementation of equalizers using ANN provides an upper hand over the performance and computational complexity with respect to conventional methods