CN201418137Y - Lossless compression processing system for spaceborne image - Google Patents
Lossless compression processing system for spaceborne image Download PDFInfo
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- CN201418137Y CN201418137Y CN 200820123864 CN200820123864U CN201418137Y CN 201418137 Y CN201418137 Y CN 201418137Y CN 200820123864 CN200820123864 CN 200820123864 CN 200820123864 U CN200820123864 U CN 200820123864U CN 201418137 Y CN201418137 Y CN 201418137Y
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Abstract
The utility model relates to a lossless compression pressing system for spaceborne image. The processing system comprises the following components: a control module, a JPEG-LS prediction module, an RICE coding module and a code stream splicing module. The processing module comprises the following steps: grouping the input image data through a control module according 16 sampling points; transmitting the grouped data into a preprocessing module for performing decorrelation processing for obtaining one group of data which are independent from one another; and then obtaining the compression codesteam through the RICE coding module and the code steam splicing module. The lossless compression processing system for spaceborne image according to the utility model has high lossless compression coding efficiency. Furthermore as the core design of the processing system aims at the initial data calculation after grouping, the resynchronization mechanism which adopts decoding can prevent the error code diffusion in the spatial data transmission. The power consumption is low and the requirement of lossless compression of spaceboren image is satisfied.
Description
Technical Field
The utility model relates to a lossless compression processing system of image especially relates to a lossless compression processing system of spaceborne image suitable for space application.
Background
In recent years, with the rapid development of aerospace technology in China, the number and the precision of on-board sensing and detecting equipment are greatly increased compared with the prior art, massive data is formed, and certain difficulty is caused in on-board data storage and downlink transmission. Because the communication bandwidth of the spacecraft is limited, the capacity of a storage device cannot be increased without limit, so that on-orbit image compression becomes a necessary link for on-board data processing, and the development of a high-performance satellite-borne image compression system becomes an urgent task.
How to optimally realize a high-speed image lossless compression system under the requirements of strict power consumption, weight and volume depends on the design scheme. Although an embedded system using a general-purpose microprocessor or a DSP as a core can complete a compression algorithm relatively conveniently, there is a serious drawback that a high clock frequency (over 200MHz) is required to meet a real-time requirement, which causes a series of problems of electromagnetic compatibility and causes inconvenience in design.
SUMMERY OF THE UTILITY MODEL
An object of the utility model is to overcome above-mentioned prior art not enough to a satellite-borne image lossless compression processing system is provided.
In order to achieve the purpose, the utility model discloses a satellite-borne image lossless compression processing system adopts the preprocessing module based on JPEG-LS based on the image compression algorithm that CCSDS121.0-B-1 recommends, has effectively improved the compression ratio. The system comprises: the device comprises a preprocessing module based on a JPEG-LS prediction mode, a RICE entropy coding module and a code word splicing module.
The preprocessing module receives n-bit image data DataIn through a data line each time, a signal NewBlock indicates whether the image data is the start of a new data Block, if the NewBlock signal is set, the preprocessing module starts to preprocess the image data of the data Block, namely, decorrelation processing, and sends a preprocessing result DataOut to the RICE entropy coding module through the data line.
And the RICE entropy coding module codes the preprocessing result DataOut to generate a code Word with an indefinite code length Len, and sends the code Word and the code length Len to the code Word splicing module, and sets an EndBlock signal after the current Block coding is finished.
And the code Word splicing module receives the code Word and the code length Len, and the spliced code stream is output according to the 8-bit or 16-bit fixed-length code Word Byte.
The input image data is grouped according to 16 sampling points through a control module, the grouped data enters a preprocessor for decorrelation processing to obtain a group of mutually independent data, and then a compressed code stream is obtained through an RICE encoder and a code stream splicing circuit.
Wherein the preprocessing module comprises: a predictor and a mapper.
The predictor subtracts the predicted value DataPre of the corresponding pixel from the image data input as n bits to obtain data DataMap of n +1 bits.
The mapper performs mapping transformation on the data DataMap to generate a preprocessing result DataOut, wherein the preprocessing result DataOut is a prediction residual sequence with approximate geometric distribution.
Wherein the RICE entropy coding module comprises: an accumulator, a selector and an encoder.
And the accumulator accumulates the preprocessing result DataOut to obtain Sum Sum and sends the Sum Sum to the selector.
And the selector performs table look-up operation on the Sum to obtain an encoding option K.
And the encoder encodes the preprocessing result DataOut according to the encoding option K, outputs a codeword Word and a code length Len, and sets EndBlock after the Block encoding of the data Block is finished.
The utility model has the advantages that:
1. the utility model discloses a satellite-borne image lossless compression processing system that accords with CCSDS standard is lossless to compress the code efficient (utilize the experimental image that CCSDS provided to test, and lossless compression ratio is on average 2.0).
2. The utility model discloses a star carries image lossless compression processing system's core design is to grouping back original data operation, adopts the resynchronization mechanism of decoding can prevent the error code diffusion in the space data transmission, low power dissipation (less than or equal to 1 watt/Msamples/sec).
3. The utility model discloses a satellite-borne image lossless compression processing system supports the input image format based on Frame (Frame) and the input image format based on Strip (Strip), is suitable for near-earth observation and deep space exploration task.
Drawings
FIG. 1 is a block diagram of a prior art lossless compression algorithm;
FIG. 2 is a top-level design circuit block diagram of the satellite-borne image lossless compression processing system of the present invention;
FIG. 3 is a block circuit diagram of a preprocessing module in the lossless compression processing system for satellite-borne images of the present invention;
FIG. 4 is a block diagram of a predictor in the lossless compression processing system for satellite-borne images according to the present invention;
FIG. 5 is a schematic diagram of the pixel position of the prediction method in the lossless compression processing system for satellite-borne images according to the present invention;
FIG. 6 is a circuit block diagram of the RICE entropy coding module in the lossless compression processing system for satellite-borne images.
Detailed Description
CCSDS published a lossless compression standard for spatial science data in 1997 (CCSDS121.0-B-1), suggesting the use of RICE algorithm, as shown in FIG. 1. The utility model discloses improved this algorithm and carried out high-speed hardware to the algorithm after improving and realize.
The utility model provides a lossless compression processing system of image includes: a preprocessing module based on a JPEG-LS prediction mode and an adaptive entropy coding module. The pre-processing module comprises a predictor and a mapper, wherein the predictor removes the data correlation and maps the data correlation into characteristic values beneficial to entropy coding, and the characteristic values are subjected to adaptive entropy coding to obtain a good compression effect. The entropy coding module is a collection of variable length coders, and selects a coder with the highest compression ratio to transmit together with the identifier. Since each block (J pre-processed samples) can select coding modes (four modes of basic sequence coding, split sample coding, low entropy coding and non-compression coding), the RICE algorithm can adapt to the variation of information source statistical characteristics.
The utility model discloses a pretreatment module based on JPEG-LS has effectively improved the compression ratio.
General architecture of image lossless compression processing system
The utility model discloses an algorithm that lossless compression processing system adopted is based on CCSDS121.0-B-1 data compression algorithm, compression processing system's top layer structure, as shown in FIG. 2. The method mainly comprises the following steps: a preprocessing module PrePrcessor and an entropy coding module Encoder. The preprocessing module receives n-bit image data through a data line DataIn each time, a signal NewBlock indicates whether a new Block starts, and if the signal is set, preprocessing is started on the image data of the Block; the result of the preprocessing is sent to a RICE entropy coder through a data line DataOut to generate a code Word of an indefinite code length Len, and an EndBlock signal is set after the current Block coding is finished; and the code Word splicing module ByteBuilder receives the code Word and the code length Len, and the spliced code stream is output according to the fixed-length code Word Byte.
Pre-processing module
The hardware structure design of the preprocessing module, as shown in fig. 3, mainly includes a Predictor (as shown in fig. 4) and a Mapper. The invention adopts a JPEG-LS predictor, and under the condition that the pixel position is as shown in figure 5, the JPEG-LS predictor is as follows:
wherein,
is x0The predicted value of (2).
The input image data DataIn is n bits, and is subtracted from the predicted value DataPre of the corresponding pixel to obtain n +1 bit data DataMap. The Mapper maps the DataMap to generate output data DataOut. Predicted results
Selecting by a multiplexer with the values to be selected being min (x)1，x3)，max(x1，x3) And x1-x2+x3(ii) a And then the prediction residual sequence with approximate geometric distribution can be obtained through mapping.
Entropy coding module
The RICE entropy coding module structure is shown in FIG. 6. The Accumulator accumulates the output DataOut of the PreProcessor PreProcessor, sends the Sum Sum to the Selector, and performs a simple table look-up operation in the Selector to obtain an optimal coding option K; and the Encoder Encoder encodes the DataOut according to the optimal encoding option K, outputs a codeword Word and a code length Len, and sets EndBlock after the encoding of the data block is finished.
Claims (3)
1. A lossless compression processing system for satellite-borne images, the system comprising: a preprocessing module for decorrelation processing based on a JPEG-LS prediction mode, a RICE entropy coding module for generating a code Word of an indefinite code length Len and a code Word splicing module;
the preprocessing module receives n-bit image data dataIn through a data line each time, and sends a preprocessing result dataOut to the RICE entropy coding module through the data line after decorrelation preprocessing;
the RICE entropy coding module is used for coding the preprocessing result DataOut and then sending a code Word and a code length Len into the code Word splicing module;
and the code Word splicing module outputs the spliced code stream of the code Word and the code length Len according to the fixed-length code Word Byte.
2. The on-board image lossless compression processing system according to claim 1, wherein the preprocessing module includes: a predictor and a mapper.
3. The on-board image lossless compression processing system according to claim 1, wherein the RICE entropy coding module includes: an accumulator, a selector and an encoder;
the accumulator accumulates the preprocessing result DataOut to obtain a sum and sends the sum to the selector;
the selector performs table look-up operation on the sum to obtain a coding option K;
and the encoder encodes the preprocessing result DataOut according to the encoding option K and outputs a codeword Word and a code length Len.
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| CN 200820123864 CN201418137Y (en) | 2008-11-21 | 2008-11-21 | Lossless compression processing system for spaceborne image |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102215385A (en) * | 2010-04-09 | 2011-10-12 | 中国科学院沈阳自动化研究所 | Real-time lossless compression method for image |
| CN111510643A (en) * | 2019-01-31 | 2020-08-07 | 杭州海康威视数字技术股份有限公司 | System and method for splicing panoramic image and close-up image |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102215385A (en) * | 2010-04-09 | 2011-10-12 | 中国科学院沈阳自动化研究所 | Real-time lossless compression method for image |
| CN102215385B (en) * | 2010-04-09 | 2014-03-19 | 中国科学院沈阳自动化研究所 | Real-time lossless compression method for image |
| CN111510643A (en) * | 2019-01-31 | 2020-08-07 | 杭州海康威视数字技术股份有限公司 | System and method for splicing panoramic image and close-up image |
| CN111510643B (en) * | 2019-01-31 | 2022-09-30 | 杭州海康威视数字技术股份有限公司 | System and method for splicing panoramic image and close-up image |
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