A fully embedded two-stage coder for hyperspectral near-lossless compression

This letter proposes a near-lossless coder for hyperspectral images. The coding technique is fully embedded and minimizes the distortion in the l2 norm initially and in the l∞ norm subsequently. Based on a two-stage near-lossless compression scheme, it includes a lossy and a near-lossless layer. The novelties are: the observation of the convergence of the entropy of the residuals in the original domain and in the spectral-spatial transformed domain; and an embedded near-lossless layer. These contributions enable a progressive transmission while optimising both SNR and PAE performance. The embeddedness is accomplished by bitplane encoding plus arithmetic encoding. Experimental results suggest that the proposed method yields a highly competitive coding performance for hyperspectral images, outperforming multi-component JPEG2000 for l∞ norm and pairing its performance for l2 norm, and also outperforming M-CALIC in the near-lossless case -for PAE ≥5-

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

Lossy-to-lossless 3D image coding through prior coefficient lookup tables

This paper describes a low-complexity, highefficiency, lossy-to-lossless 3D image coding system. The proposed system is based on a novel probability model for the symbols that are emitted by bitplane coding engines. This probability model uses partially reconstructed coefficients from previous components together with a mathematical framework that captures the statistical behavior of the image. An important aspect of this mathematical framework is its generality, which makes the proposed scheme suitable for different types of 3D images. The main advantages of the proposed scheme are competitive coding performance, low computational load, very low memory requirements, straightforward implementation, and simple adaptation to most sensors