Bell image compression method based on jpeg2000 standard

Abstract

The invention discloses a JPEG2000 standard-based Bayer image compression method. The method comprises following specific steps of (1) imputing a to-be-compressed Bayer image; (2) calculating a targetcode rate; (3) separating color components; (4) performing discrete cosine transform; (5) performing wavelet transform; (6) quantifying; (7) performing T1 coding; (8) calculating a rate-distortion slope threshold; (9) jointly intercepting a code rate; (10) calculating a total cut-off code rate; (11) determining sizes of the cut-off code rate and the target code rate; (12) organizing a code stream; and (13) outputting a compressed file. Steps of 1*4 integral discrete cosine transform and jointly intercepting the code rate effectively overcome defect of low compression performance in the priorart, the step of performing T1 coding overcomes the defect of low error resilience in the prior art, so that the method can be used for compressing and coding the Bayer image in remote sensing satellite.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a Bell image compression method based on the JPEG2000 standard in the technical field of image compression. The invention can be used for compressing and coding Bell images in remote sensing satellites. Background technique [0002] Remote sensing images obtained by satellite cameras are divided into two types: black and white and color. At present, most satellite cameras are light, small and low-power area array CMOS cameras. Then restore it to a color image by interpolation estimation. Early Bayer image compression used in remote sensing satellite CMOS cameras used the method of first compressing the image and then restoring it to a color image. However, the red, green, and blue components in the Bayer image were extracted and compressed separately during compression, without considering the relationship between the three color components. Color correlation, en...

AI Technical Summary

Problems solved by technology

The shortcomings of this method are: when performing space transformation, this method simply takes the average value of the green component data, which will lose the information of the green component, resulting in low compression performance; and this method uses the SPIHT compression algorithm, Poor anti-error performance

Although this method removes the color correlation between the three color components of the Bell Bayer image, there are still shortcomings in this method: the method uses the SPIHT compression algorithm, which has poor anti-error performance and is not suitable for the harsh environment of remote sensing satellites

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