Correlation-Based Band-Ordering Heuristic for Lossless Compression of Hyperspectral Sounder Data

Toivanen, Pekka; Kubasova, Olga; Mielikäinen, Jarno

doi:10.1109/lgrs.2004.838410

Cited by 43 publications

(27 citation statements)

References 16 publications

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“…Satellite image compression methods can be divided into two methods: prediction-based and transformation-based. Prediction-based methods [16][17][18] use encoded pixels to estimate the current pixel value based on the correlation between pixels or bands of satellite images. Transformation-based methods [19,20] regard satellite data as a generalized, stationary random field [21]; its three-dimensional orthogonal transformation [22] maximizes the information concentrated in a small number of transform coefficients, thereby removing the maximum amount of spatial redundancy and inter-spectrum redundancy.…”

Section: Video Compression Of Satellite Videosmentioning

confidence: 99%

Towards Real-Time Service from Remote Sensing: Compression of Earth Observatory Video Data via Long-Term Background Referencing

Xiao

Zhu

et al. 2018

Remote Sensing

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City surveillance enables many innovative applications of smart cities. However, the real-time utilization of remotely sensed surveillance data via unmanned aerial vehicles (UAVs) or video satellites is hindered by the considerable gap between the high data collection rate and the limited transmission bandwidth. High efficiency compression of the data is in high demand. Long-term background redundancy (LBR) (in contrast to local spatial/temporal redundancies in a single video clip) is a new form of redundancy common in Earth observatory video data (EOVD). LBR is induced by the repetition of static landscapes across multiple video clips and becomes significant as the number of video clips shot of the same area increases. Eliminating LBR improves EOVD coding efficiency considerably. First, this study proposes eliminating LBR by creating a long-term background referencing library (LBRL) containing high-definition geographically registered images of an entire area. Then, it analyzes the factors affecting the variations in the image representations of the background. Next, it proposes a method of generating references for encoding current video and develops the encoding and decoding framework for EOVD compression. Experimental results show that encoding UAV video clips with the proposed method saved an average of more than 54% bits using references generated under the same conditions. Bitrate savings reached 25-35% when applied to satellite video data with arbitrarily collected reference images. Applying the proposed coding method to EOVD will facilitate remote surveillance, which can foster the development of online smart city applications.

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Section: Video Compression Of Satellite Videosmentioning

confidence: 99%

Towards Real-Time Service from Remote Sensing: Compression of Earth Observatory Video Data via Long-Term Background Referencing

Xiao

Zhu

et al. 2018

Remote Sensing

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“…To optimize this overhead generated by the predictor parameters, we employ two different configurations: at low rates we use AE ¿ ¾ , with 10 bits for and 6 bits for «, whereas at high rates we use AE ½ , with 16 bits for and 10 bits for «. In addition, we also test a configuration in which optimal band reordering is used, as in [7].…”

Section: Resultsmentioning

confidence: 99%

Low-complexity lossy compression of hyperspectral images via informed quantization

Abrardo

Barni

Magli

2010

2010 IEEE International Conference on Image Processing

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Lossy compression of hyperspectral and ultraspectral images is traditionally performed using 3D transform coding. This approach yields good performance, but the complexity and memory requirements make it unsuitable for onboard compression. In this paper we propose a low-complexity lossy compression scheme based on prediction, quantization and rate-distortion optimization. The scheme employs coset codes coupled with the newconcept of "informed quantization", and requires no entropy coding. The performance of the resulting algorithm is competitive with that of stateof- the-art 3D transform coding schemes, but the complexity is immensely lower, making it suitable for onboard compression at high throughputs

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“…Also other lossless coding methods exist, e.g. they are based on band ordering (Tate, 1997;Toivanen et al, 2005) or spectral and spatial noncausal prediction (Memon et al, 1994). Thus, in the lossless compression of spectral images better results are achieved through the transform coding and especially, with the predictive coding.…”

Section: Lossless Compression Of Spectral Imagesmentioning

confidence: 99%

Compression of Spectral Images

Kaarna¹

2007

Vision Systems: Segmentation and Pattern Recognition

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Correlation-Based Band-Ordering Heuristic for Lossless Compression of Hyperspectral Sounder Data

Cited by 43 publications

References 16 publications

Towards Real-Time Service from Remote Sensing: Compression of Earth Observatory Video Data via Long-Term Background Referencing

Towards Real-Time Service from Remote Sensing: Compression of Earth Observatory Video Data via Long-Term Background Referencing

Low-complexity lossy compression of hyperspectral images via informed quantization

Compression of Spectral Images

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