CN201418137Y - A Lossless Compression Processing System for Spaceborne Images - Google Patents
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Abstract
本实用新型涉及一种星载图像无损压缩处理系统。该处理系统包括:控制模块、JPEG-LS预测模块、RICE编码模块和码流拼接模块,其处理过程是:输入的图像数据经控制模块按16个采样点分组;分组后的数据进入预处理模块进行去相关处理,得到一组相互独立的数据,再经RICE编码模块和码流拼接模块得到压缩码流。本实用新型的无损压缩编码效率高,且由于处理系统的核心设计是针对分组后原始数据进行运算,采用解码的重同步机制可以防止空间数据传输中的误码扩散,功耗低,满足星载图像无损压缩的要求。
The utility model relates to a lossless compression processing system for satellite images. The processing system includes: a control module, a JPEG-LS prediction module, a RICE coding module and a code stream splicing module. The processing process is: the input image data is grouped by 16 sampling points through the control module; the grouped data enters the preprocessing module Perform decorrelation processing to obtain a set of mutually independent data, and then obtain a compressed code stream through the RICE encoding module and the code stream splicing module. The lossless compression coding efficiency of the utility model is high, and since the core design of the processing system is to perform operations on the original data after grouping, the resynchronization mechanism of decoding can prevent the spread of error codes in the space data transmission, and the power consumption is low, which meets the requirements of spaceborne Image lossless compression requirements.
Description
技术领域 technical field
本实用新型涉及一种图像无损压缩处理系统,特别是涉及一种适用于空间应用的星载图像无损压缩处理系统。The utility model relates to an image lossless compression processing system, in particular to a space-borne image lossless compression processing system suitable for space applications.
背景技术 Background technique
近年来,随着我国航天技术的迅猛发展,星上传感和探测设备无论在数量还是精度上都比以前大大增加,形成了海量数据,从而对星上数据存储和下行传输造成了一定的困难。由于航天器通信带宽有限,不可能无限制的增大存储设备的容量,于是在轨图像压缩就成为星上数据处理的一个必备环节,而开发高性能星载图像压缩系统也成为当务之急。In recent years, with the rapid development of my country's aerospace technology, the number and accuracy of on-board sensing and detection equipment have increased greatly compared to before, forming massive data, which has caused certain difficulties for on-board data storage and downlink transmission. Due to the limited communication bandwidth of the spacecraft, it is impossible to increase the capacity of the storage device without limit, so on-orbit image compression has become a necessary part of on-board data processing, and the development of a high-performance on-board image compression system has also become a top priority.
如何能在具有严格功耗、重量、体积的要求下,最优的实现高速图像无损压缩系统,取决于设计的方案。以通用微处理器或者DSP为核心的嵌入式系统,虽然能够相对较为方便的完成压缩算法,但存在一个严重的缺陷--需要较高的时钟频率(超过200MHz),才能满足实时性要求,这将引发一系列电磁兼容问题,带来设计上的诸多不便。How to optimally implement a high-speed image lossless compression system with strict power consumption, weight, and volume requirements depends on the design scheme. Although the embedded system with general-purpose microprocessor or DSP as the core can complete the compression algorithm relatively conveniently, there is a serious defect - a higher clock frequency (more than 200MHz) is needed to meet the real-time requirements. It will cause a series of electromagnetic compatibility problems and bring a lot of inconvenience in design.
实用新型内容 Utility model content
本实用新型的目的在于,为了克服上述现有技术的不足,从而提供了一种星载图像无损压缩处理系统。The purpose of this utility model is to provide a lossless compression processing system for spaceborne images in order to overcome the above-mentioned deficiencies in the prior art.
为实现上述目的,本实用新型的星载图像无损压缩处理系统基于CCSDS121.0-B-1推荐的图像压缩算法,采用基于JPEG-LS的预处理模块,有效提高了压缩比。该系统包括:基于JPEG-LS预测方式的预处理模块,RICE熵编码模块和码字拼接模块。In order to achieve the above purpose, the lossless compression processing system for spaceborne image of the utility model is based on the image compression algorithm recommended by CCSDS121.0-B-1, and adopts the preprocessing module based on JPEG-LS, which effectively improves the compression ratio. The system includes: a preprocessing module based on the JPEG-LS prediction method, a RICE entropy coding module and a code word splicing module.
所述预处理模块通过数据线每次接收一个n位的图像数据DataIn,信号NewBlock表示是否为一个新数据块Block的开始,若NewBlock信号置位则开始对该数据块Block的图像数据进行预处理,即去相关处理,并将预处理结果DataOut通过数据线送入RICE熵编码模块。The preprocessing module receives an n-bit image data DataIn each time through the data line, and the signal NewBlock indicates whether it is the beginning of a new data block Block, and if the NewBlock signal is set, the image data of the data block Block is preprocessed , that is, decorrelation processing, and the preprocessing result DataOut is sent to the RICE entropy coding module through the data line.
所述RICE熵编码模块对预处理结果DataOut进行编码,产生不定码长Len的码字Word,并将码字Word和码长Len送入码字拼接模块,当前Block编码结束后,置位EndBlock信号。The RICE entropy encoding module encodes the preprocessing result DataOut to generate a code word Word with an indeterminate code length Len, and sends the code word Word and the code length Len to the code word splicing module. After the current Block encoding is completed, the EndBlock signal is set .
所述码字拼接模块接收码字Word和码长Len,拼接码流按8位或16位定长码字Byte输出。The code word stitching module receives the code word Word and the code length Len, and outputs the spliced code stream as 8-bit or 16-bit fixed-length code word Byte.
其中,输入的图像数据经控制模块按16个采样点分组,分组后的数据进入预处理器进行去相关处理得到一组相互独立的数据,再经RICE编码器和码流拼接电路得到压缩码流。Among them, the input image data is grouped by 16 sampling points through the control module, and the grouped data enters the preprocessor for decorrelation processing to obtain a set of mutually independent data, and then the compressed code stream is obtained by the RICE encoder and the code stream splicing circuit .
其中,所述预处理模块包括:预测器和映射器。Wherein, the preprocessing module includes: a predictor and a mapper.
所述预测器将输入为n比特的图像数据与对应像素的预测值DataPre相减,得到n+1比特的数据DataMap。The predictor subtracts the n-bit input image data from the predicted value DataPre of the corresponding pixel to obtain n+1-bit data DataMap.
所述映射器对数据DataMap做映射变换,产生预处理结果DataOut,该预处理结果DataOut为近似几何分布的预测残差序列。The mapper performs mapping transformation on the data DataMap to generate a preprocessing result DataOut, and the preprocessing result DataOut is a prediction residual sequence of an approximate geometric distribution.
其中,所述RICE熵编码模块包括:累加器、选择器和编码器。Wherein, the RICE entropy encoding module includes: an accumulator, a selector and an encoder.
所述累加器对预处理结果DataOut进行累加得到和Sum,并将和Sum送入选择器。The accumulator accumulates the preprocessing result DataOut to obtain a sum, and sends the sum to the selector.
所述选择器对和Sum进行查表操作,得到编码选项K。The selector performs a table look-up operation on Sum to obtain the coding option K.
所述编码器根据编码选项K对预处理结果DataOut进行编码,输出码字Word和码长Len,当本数据块Block编码结束后置位EndBlock。The encoder encodes the preprocessing result DataOut according to the encoding option K, outputs the code word Word and the code length Len, and sets EndBlock when the encoding of the data block Block is completed.
本实用新型的优点在于:The utility model has the advantages of:
1、本实用新型的符合CCSDS标准的星载图像无损压缩处理系统无损压缩编码效率高(利用CCSDS提供的实验图像进行测试,无损压缩比平均为2.0)。1. The on-board image lossless compression processing system conforming to the CCSDS standard of the utility model has high lossless compression encoding efficiency (using the experimental image provided by CCSDS to test, the average lossless compression ratio is 2.0).
2、本实用新型的星载图像无损压缩处理系统的核心设计是针对分组后原始数据进行运算,采用解码的重同步机制可以防止空间数据传输中的误码扩散,功耗低(≤1watt/Msamples/sec)。2. The core design of the spaceborne image lossless compression processing system of the present utility model is to perform calculations on the original data after grouping, and the resynchronization mechanism of decoding can prevent the spread of errors in space data transmission, and the power consumption is low (≤1watt/Msamples /sec).
3、本实用新型的星载图像无损压缩处理系统支持基于帧(Frame)的输入图像格式和基于条带(Strip)的输入图像格式,适合于近地观测和深层空间探测任务。3. The spaceborne image lossless compression processing system of the present invention supports frame-based input image formats and strip-based input image formats, and is suitable for near-earth observation and deep space exploration tasks.
附图说明 Description of drawings
图1是现有技术的无损压缩算法的框图;Fig. 1 is the block diagram of the lossless compression algorithm of prior art;
图2是本实用新型星载图像无损压缩处理系统的顶层设计电路框图;Fig. 2 is the top-level design circuit block diagram of the utility model's spaceborne image lossless compression processing system;
图3是本实用新型星载图像无损压缩处理系统中预处理模块的电路框图;Fig. 3 is the circuit block diagram of the preprocessing module in the lossless compression processing system of the starborne image of the present invention;
图4是本实用新型星载图像无损压缩处理系统中预测器的电路框图;Fig. 4 is the circuit block diagram of predictor in the utility model space-borne image lossless compression processing system;
图5是本实用新型星载图像无损压缩处理系统中预测方法的像素位置示意图;Fig. 5 is a schematic diagram of the pixel positions of the prediction method in the spaceborne image lossless compression processing system of the present invention;
图6是本实用新型星载图像无损压缩处理系统中RICE熵编码模块的电路框图。Fig. 6 is a circuit block diagram of the RICE entropy encoding module in the lossless compression processing system for the spaceborne image of the present invention.
具体实施方式 Detailed ways
CCSDS在1997年公布了适用于空间科学数据的无损压缩标准(CCSDS121.0-B-1),建议采用RICE算法,如图1所示。本实用新型对该算法进行了改进并对改进后算法进行了高速硬件实现。CCSDS announced the lossless compression standard (CCSDS121.0-B-1) suitable for space science data in 1997, and it is recommended to use the RICE algorithm, as shown in Figure 1. The utility model improves the algorithm and realizes the improved algorithm with high-speed hardware.
本实用新型提供的图像无损压缩处理系统包括:基于JPEG-LS预测方式的预处理模块和自适应熵编码模块。预处理模块包含预测器和映射器,预测器去除了数据的相关性,然后映射它们为利于熵编码的特征值,对这些特征值进行自适应熵编码可以得到很好的压缩效果。熵编码模块是一系列变长编码器的集合,选定一种最高压缩比的编码器与标识符一起传输。由于每块(J个前处理样本)均可选择编码模式(基本序列编码、分裂样本编码、低熵值编码和无压缩编码四种模式),所以RICE算法能适应信源统计特性的变化。The image lossless compression processing system provided by the utility model includes: a preprocessing module based on JPEG-LS prediction mode and an adaptive entropy coding module. The preprocessing module includes a predictor and a mapper. The predictor removes the correlation of the data, and then maps them to feature values that are conducive to entropy coding. Adaptive entropy coding for these feature values can get a good compression effect. The entropy coding module is a collection of a series of variable length coders, and a coder with the highest compression ratio is selected to be transmitted together with the identifier. Since each block (J pre-processing samples) can choose a coding mode (basic sequence coding, split sample coding, low entropy coding and uncompressed coding four modes), so the RICE algorithm can adapt to the change of the statistical characteristics of the source.
本实用新型采用基于JPEG-LS的预处理模块,有效提高了压缩比。The utility model adopts a preprocessing module based on JPEG-LS, which effectively improves the compression ratio.
图像无损压缩处理系统总体架构Overall Architecture of Image Lossless Compression Processing System
本实用新型的无损压缩处理系统采用的算法基于CCSDS 121.0-B-1数据压缩算法,压缩处理系统的顶层结构,如图2所示。主要包括:预处理模块PrePrcessor和熵编码模块Encoder。预处理模块通过数据线DataIn每次接收一个n位的图像数据,信号NewBlock表示是否为一个新Block的开始,若此信号置位则开始对这个Block的图像数据进行预处理;预处理的结果通过数据线DataOut送入RICE熵编码器产生不定码长Len的码字Word,当前Block编码结束后,置位EndBlock信号;码字拼接模块ByteBuilder接收码字Word和码长Len,拼接码流按定长码字Byte输出。The algorithm adopted by the lossless compression processing system of the utility model is based on the CCSDS 121.0-B-1 data compression algorithm, and the top-level structure of the compression processing system is shown in Figure 2. It mainly includes: preprocessing module PrePrcessor and entropy coding module Encoder. The preprocessing module receives an n-bit image data each time through the data line DataIn, and the signal NewBlock indicates whether it is the beginning of a new Block. If this signal is set, it starts to preprocess the image data of this Block; the preprocessing result is passed The data line DataOut is sent to the RICE entropy encoder to generate a code word Word with an indefinite code length Len. After the current Block encoding is completed, the EndBlock signal is set; the code word splicing module ByteBuilder receives the code word Word and the code length Len, and the spliced code stream is fixed-length Codeword Byte output.
预处理模块preprocessing module
预处理模块硬件结构设计,如图3所示,主要包括预测器Predictor(如图4)和映射器Mapper。本发明采用JPEG-LS预测器,在像素位置如图5所示的情况下,JPEG-LS预测器如下:The hardware structure design of the preprocessing module, as shown in Figure 3, mainly includes the predictor Predictor (Figure 4) and the mapper Mapper. The present invention adopts JPEG-LS predictor, under the situation of pixel position as shown in Figure 5, JPEG-LS predictor is as follows:
其中,为x0的预测值。in, is the predicted value of x0 .
其中,输入图像数据DataIn为n比特,与对应像素的预测值DataPre相减,得到n+1比特的数据DataMap。映射器Mapper对DataMap做映射变换,产生输出数据DataOut。预测结果由多路选择器进行选择,待选值分别为min(x1,x3),max(x1,x3)和x1-x2+x3;再通过映射可以得到近似几何分布的预测残差序列。Wherein, the input image data DataIn is n bits, which is subtracted from the predicted value DataPre of the corresponding pixel to obtain n+1 bit data DataMap. Mapper performs mapping transformation on DataMap to generate output data DataOut. forecast result Selected by the multiplexer, the values to be selected are min(x 1 , x 3 ), max(x 1 , x 3 ) and x 1 -x 2 +x 3 ; and then the prediction of approximate geometric distribution can be obtained through mapping residual sequence.
熵编码模块Entropy Coding Module
RICE熵编码模块结构,如图6所示。其中,累加器Accumulator对预处理器PreProcessor的输出DataOut进行累加,将和Sum送入选择器Selector,在Selector中进行一个简单的查表操作,得到最优编码选项K;编码器Encoder根据最优编码选项K对DataOut进行编码,输出码字Word和码长Len,当本数据块编码结束后置位EndBlock。The RICE entropy coding module structure is shown in Figure 6. Among them, the accumulator Accumulator accumulates the output DataOut of the preprocessor PreProcessor, and sends Sum to the selector Selector, and performs a simple table lookup operation in the Selector to obtain the optimal encoding option K; the encoder Encoder according to the optimal encoding Option K encodes DataOut, outputs the codeword Word and the code length Len, and sets EndBlock when the encoding of this data block is completed.
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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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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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