Improved Grouping and Noise Cancellation for Automatic Lossy Compression of AVIRIS Images

Ponomarenko, Nikolay N.; Zriakhov, Mikhail; Kaarna, Arto

doi:10.1007/978-3-642-17691-3_24

Cited by 9 publications

(13 citation statements)

References 20 publications

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“…1,4,5 Meanwhile, this inherent property allows increasing CR by employing spectral redundancy of the data without increasing losses. 4,5,13,[38][39][40] There are numerous possible approaches to carry out 3-D compression. Our goal here is not to compare them and/or to find the best (optimal) approach.…”

Section: Compression Of Difference Images With Sub-band Groupingmentioning

confidence: 99%

“…In Ref. 40, sub-band images have been normalized before compression using the estimates of noise variance. In both cases, the noise was supposed pure additive.…”

Section: Three-dimensional Compression With Sub-band Groupingmentioning

confidence: 99%

“…Experience in color image and video compression 37 shows that if there is a correlation between multidimensional data components (e.g., frames for video), it can be used to increase the CR. Similarly, the use of three-dimensional (3-D) compression methods usually leads to sufficient increase of CR compared to component-wise compression of hyperspectral data (see, e.g., [38][39][40] ). This happens due to exploiting high interchannel correlation, which is inherent for hyperspectral images.…”

Section: Introductionmentioning

confidence: 99%

“…41 Finally, there can be different priorities of requirements to lossy compression of hyperspectral data. 4,38,40 For example, it might be required to provide CR not less than some given value with simultaneous desire to introduce less distortions into compressed data. We concentrate on another priority, the primary goal is to carry out lossy compression in the neighborhood of the optimal operation point (OOP), while it is also desirable to provide as much high CR as possible.…”

Section: Introductionmentioning

confidence: 99%

“…12,47 Third, there is also a 3-D version of AGU that has earlier been successfully applied for compressing AVIRIS hyperspectral data with sub-band grouping. 13,40 Therefore, let us come to demonstrating and analyzing typical dependences presented in Fig. 2.…”

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confidence: 99%

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Lossy compression of hyperspectral images based on noise parameters estimation and variance stabilizing transform

Zemliachenko

Kozhemiakin

Uss

et al. 2014

J. Appl. Remote Sens

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Abstract. A problem of lossy compression of hyperspectral images is considered. A specific aspect is that we assume a signal-dependent model of noise for data acquired by new generation sensors. Moreover, a signal-dependent component of the noise is assumed dominant compared to a signal-independent noise component. Sub-band (component-wise) lossy compression is studied first, and it is demonstrated that optimal operation point (OOP) can exist. For such OOP, the mean square error between compressed and noise-free images attains global or, at least, local minimum, i.e., a good effect of noise removal (filtering) is reached. In practice, we show how compression in the neighborhood of OOP can be carried out, when a noise-free image is not available. Two approaches for reaching this goal are studied. First, lossy compression directly applied to the original data is considered. According to another approach, lossy compression is applied to images after direct variance stabilizing transform (VST) with properly adjusted parameters. Inverse VST has to be performed only after data decompression. It is shown that the second approach has certain advantages. One of them is that the quantization step for a coder can be set the same for all sub-band images. This offers favorable prerequisites for applying three-dimensional (3-D) methods of lossy compression for sub-band images combined into groups after VST. Two approaches to 3-D compression, based on the discrete cosine transform, are proposed and studied. A first approach presumes obtaining the reference and "difference" images for each group. A second performs compression directly for sub-images in a group. We show that it is a good choice to have 16 sub-images in each group. The abovementioned approaches are tested for Hyperion hyperspectral data. It is demonstrated that the compression ratio of about 15-20 can be provided for hyperspectral image compression in the neighborhood of OOP for 3-D coders, which is sufficiently larger than for component-wise compression and lossless coding.

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Section: Compression Of Difference Images With Sub-band Groupingmentioning

confidence: 99%

“…In Ref. 40, sub-band images have been normalized before compression using the estimates of noise variance. In both cases, the noise was supposed pure additive.…”

Section: Three-dimensional Compression With Sub-band Groupingmentioning

confidence: 99%