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Progressive distribution type encoding and decoding method and device for multispectral image

An encoding and decoding method and multi-spectral image technology are applied in the field of progressive distributed encoding and decoding and devices for multi-spectral images, and can solve problems such as being unsuitable for satellite applications, unable to effectively compress multi-spectral images, and exceeding satellite carrying capacity. , to achieve the effect of high compression efficiency, strong error resistance, and improved fault tolerance

Inactive Publication Date: 2010-02-03
UNIV OF SCI & TECH OF CHINA
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0007] The new generation of image compression standard - JPEG2000 provides the progressive transmission characteristics of images, but it cannot effectively compress multi-spectral images
The 3-D DWT scheme, as an extension of JPEG2000, not only improves the compression efficiency, but also inherits the progressive transmission characteristics of JPEG2000. carrying capacity
Therefore, none of the above methods are suitable for satellite applications

Method used

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  • Progressive distribution type encoding and decoding method and device for multispectral image
  • Progressive distribution type encoding and decoding method and device for multispectral image
  • Progressive distribution type encoding and decoding method and device for multispectral image

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Embodiment 1

[0034] figure 1 Shows the structure of the device adopting the distributed coding method of the present invention, the set of devices includes an encoding end and a decoding end, wherein the encoding end includes a sampling unit, a bit-plane extraction unit, a binary LDPC encoding unit and a statistic calculation unit, and the decoding end It includes a sampling unit, a linear prediction unit, a prediction estimation unit, an LDPC decoding unit, an image reconstruction unit, and a mining data correlation part composed of an image segmentation unit, an area-based adaptive prediction unit and a correlation noise statistic estimation unit in series, wherein The main role of the mining data correlation part is to generate side information of the current band image from the decoded image information.

[0035] According to the distributed coding theory of the present invention, the first band adopts a traditional intra (intra-frame prediction) mode coding scheme, preferably JPEG2000...

Embodiment 2

[0089] Since multispectral images are affected by sensor imaging noise, and quantization errors are introduced in the process of signal acquisition and digitization, even if lossless coding is used, it is actually lossy. Secondly, the compression efficiency of lossless coding is generally low, which is not conducive to channel transmission. Therefore, the actual system sometimes allows lossy compression, but the error must be controlled to ensure the credibility of the signal. Generally, it is required that the MAD (maximum absolute difference) between the reconstructed image and the original image does not exceed a certain threshold. When the MAD is less than half of the background noise variance, the reconstructed image not only looks the same as the original image, but also the result of post-processing is likely to be the same as the result of directly processing the original image, that is to say, this lossy Compression with virtually no loss of information is often refer...

Embodiment 3

[0095] Considering that errors may occur due to interference when signals are transmitted in channels, the distributed encoding and decoding method provided by the present invention adopts a joint source-channel decoding scheme. The structure of the system is similar to that of the first embodiment, the difference is that the accompanying error correction problem needs to be considered when encoding and decoding LDPC. In LDPC encoding, it is the same as channel encoding, codeword c=xG, where G is a generator matrix, and only the parity part of the codeword is transmitted. When LDPC decoding is similar to channel decoding, the difference is that the noise model needs to be considered in two parts: the noise model corresponding to the information bit is determined by the statistical correlation between the source and side information, and the noise model corresponding to the parity bit is determined by the actual channel .

[0096] If the channel error exceeds the error correct...

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Abstract

The invention discloses a progressive distribution type encoding and decoding method for a multispectral image. In the method, each band image is directly and independently encoded at an encoding end,and data dependence is only excavated at a decoding end, therefore, the complexity of data calculation can be transferred from the encoding end with resource constraint to the decoding end with richresources so as to optimally utilize resources of a system and be suitable for a remote sensing satellite. The invention also discloses a distribution type encoding and decoding device for a multispectral image. A module which is used for excavating the data dependence is arranged at the decoding end, and therefore, the encoding end is simplified to the maximum to enable the system to be more reasonable. The invention has the advantages of low encoding complexity, high compression efficiency, strong error resilience ability and progressive transfer characteristic and is suitable for lossless compression or nearly lossless compression of the multispectral image, in particular to an image compression application on the satellite.

Description

technical field [0001] The present invention relates to an image processing method and device, more specifically to a progressive distributed encoding and decoding method and device for multispectral images. Background technique [0002] With the continuous development of remote sensing technology, the spatial resolution and inter-spectral resolution of the acquired images are getting higher and higher, resulting in a surge in the amount of data. In the process of transmitting from remote sensing satellites to ground receiving stations, the huge amount of data and limited There will be a contradiction between the bandwidth, so the lossless compression method that can save data and ensure image quality is widely used in the field of remote sensing image processing. [0003] At present, the lossless compression coding scheme based on Shannon's theorem is mainly divided into two steps. First, predict or transform to mine data correlation to remove redundancy, and then use Huffm...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04N7/32H04N19/90
Inventor 张金荣张威李厚强
Owner UNIV OF SCI & TECH OF CHINA
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