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

Fast and Lightweight Rate Control for Onboard Predictive Coding of Hyperspectral Images

Predictive coding is attractive for compression of hyperspecral images onboard of spacecrafts in light of the excellent rate-distortion performance and low complexity of recent schemes. In this letter we propose a rate control algorithm and integrate it in a lossy extension to the CCSDS-123 lossless compression recommendation. The proposed rate algorithm overhauls our previous scheme by being orders of magnitude faster and simpler to implement, while still providing the same accuracy in terms of output rate and comparable or better image quality

Constant-SNR, rate control and entropy coding for predictive lossy hyperspectral image compression

Predictive lossy compression has been shown to represent a very flexible framework for lossless and lossy onboard compression of multispectral and hyperspectral images with quality and rate control. In this paper, we improve predictive lossy compression in several ways, using a standard issued by the Consultative Committee on Space Data Systems, namely CCSDS-123, as an example of application. First, exploiting the flexibility in the error control process, we propose a constant-signal-to-noise-ratio algorithm that bounds the maximum relative error between each pixel of the reconstructed image and the corresponding pixel of the original image. This is very useful to avoid low-energy areas of the image being affected by large errors. Second, we propose a new rate control algorithm that has very low complexity and provides performance equal to or better than existing work. Third, we investigate several entropy coding schemes that can speed up the hardware implementation of the algorithm and, at the same time, improve coding efficiency. These advances make predictive lossy compression an extremely appealing framework for onboard systems due to its simplicity, flexibility, and coding efficiency

Multiband and Lossless Compression of Hyperspectral Images

Hyperspectral images are widely used in several real-life applications. In this paper, we investigate on the compression of hyperspectral images by considering different aspects, including the optimization of the computational complexity in order to allow implementations on limited hardware (i.e., hyperspectral sensors, etc.). We present an approach that relies on a three-dimensional predictive structure. Our predictive structure, 3D-MBLP, uses one or more previous bands as references to exploit the redundancies among the third dimension. The achieved results are comparable, and often better, with respect to the other state-of-art lossless compression techniques for hyperspectral images

Remote Sensing Data Compression

A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interestin

Information-theoretic assessment of on-board near-lossless compression of hyperspectral data

A rate-distortion model to measure the impact of near-lossless compression of raw data, that is, compression with user-defined maximum absolute error, on the information avail- able once the compressed data have been received and decompressed is proposed. Such a model requires the original uncompressed raw data and their measured noise variances. Advanced near- lossless methods are exploited only to measure the entropy of the datasets but are not required for on-board compression. In substance, the acquired raw data are regarded as a noisy realization of a noise-free spectral information source. The useful spectral information at the decoder is the mutual information between the unknown ideal source and the decoded source, which is affected by both instrument noise and compression-induced distortion. Experiments on simulated noisy images, in which the noise-free source and the noise realization are exactly known, show the trend of spectral information versus compression distortion, which in turn is related to the coded bit rate or equivalently to the compression ratio through the rate-distortion characteristic of the encoder used on satellite. Preliminary experiments on airborne visible infrared imaging spec- trometer (AVIRIS) 2006 Yellowstone sequences match the trends of the simulations. The main conclusion that can be drawn is that the noisier the dataset, the lower the CR that can be tolerated, in order to save a prefixed amount of spectral information. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. (DOI: 10.1117/1.JRS.

Image dequantization for hyperspectral lossy compression with convolutional neural networks

Significant work has been devoted to methods based on predictive coding for onboard compression of hyperspectral images. This is supported by the new CCSDS 123.0-B-2 recommendation for lossless and near-lossless compression. While lossless compression can achieve high throughput, it can only achieve limited compression ratios. The introduction of a quantizer and local decoder in the prediction loop allows to implement lossy compression with good rate-performance. However, the need to have a locally decoded version of a causal neighborhood of the current pixel under coding is a significant limiting factor in the throughput such encoder can achieve. In this work, we study the rate-distortion performance of a significantly simpler and faster onboard compressor based on prequantizing the pixels of the hyperspectral image and applying a lossless compressor (such as the lossless CCSDS CCSDS 123.0-B-2) to the quantized pixels. While this is suboptimal in terms of rate-distortion performance compared to having an in-loop quantizer, we compensate the lower quality with an on-ground post-processor based on modeling the distortion residual with a convolutional neural network. The task of the neural network is to learn the statistics of the quantization error and apply a dequantization model to restore the image