CN111654705B - Mosaic image compression method based on color space conversion - Google Patents

Mosaic image compression method based on color space conversion Download PDF

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CN111654705B
CN111654705B CN202010505297.2A CN202010505297A CN111654705B CN 111654705 B CN111654705 B CN 111654705B CN 202010505297 A CN202010505297 A CN 202010505297A CN 111654705 B CN111654705 B CN 111654705B
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CN111654705A (en
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朱树元
贺康
王忠荣
刘光辉
王正宁
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University of Electronic Science and Technology of China
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    • H04N19/00Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/169Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
    • H04N19/186Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a colour or a chrominance component
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
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Abstract

The invention belongs to the technical field of image processing, and particularly provides a mosaic image compression method based on novel color space conversion, which is used for solving the problems that the recovered image quality is low, the algorithm is complex and the method is only suitable for RGGB type CFA images with a certain specific format in the prior art. The invention converts G of CFA into G 1 RBG 2 The four-channel model is converted, then the novel color space conversion is used, the correlation among pixels is eliminated, and the digit of the coded code stream is greatly reduced, so that the coded code stream can be directly coded through a traditional coding frame; moreover, the invention is applicable to RGGB type CFA of any format; meanwhile, the color space conversion matrix and the inverse conversion matrix are both true values, but not approximate results, so that rounding errors caused by the conversion matrix are eliminated; in addition, the invention updates the brightness component to be coded in a mathematical optimization mode, thereby bringing better recovery quality.

Description

基于色彩空间转换的马赛克图像压缩方法Mosaic image compression method based on color space conversion

技术领域technical field

本发明属于图像处理技术领域,尤其涉及RGGB型CFA压缩编码领域,具体提供一种基于新型色彩空间转换的马赛克图像压缩方法。The invention belongs to the technical field of image processing, in particular to the field of RGGB type CFA compression coding, and specifically provides a mosaic image compression method based on novel color space conversion.

背景技术Background technique

为了产生彩色图像,大多数数码相机使用一块带有几种不同滤色镜的CMOS板,并使用插值技术生成全彩色图像。RGGB型彩色滤波阵列(Color Filter Array、CFA),是指在2×2区域中有两个绿色滤色镜、一个红色滤色镜和一个蓝色滤色镜的CFA的总称;其中,Bayer-CFA是最常使用的,它的每2×2区域中左上和右下为绿色滤色镜、右上为红色滤色镜、左下为滤色镜。如果插值出全彩色图像,CFA的每个像素中只有一个颜色分量,因此必须使用相邻像素信息对给定像素的其他两个颜色分量进行插值;尽管已有几种可能的插值算法,但很明显,从信息论的角度来看,它们都会导致冗余度的增加。To produce color images, most digital cameras use a CMOS board with several different color filters and use interpolation techniques to produce full-color images. RGGB color filter array (Color Filter Array, CFA) refers to the general term of CFA with two green color filters, one red color filter and one blue color filter in a 2×2 area; among them, Bayer-CFA is the most commonly used , in each 2×2 region, the upper left and lower right are green color filters, the upper right is red color filter, and the lower left is color filter. If a full-color image is interpolated, there is only one color component in each pixel of the CFA, so the other two color components of a given pixel must be interpolated using neighboring pixel information; although there are several possible interpolation algorithms, very few Obviously, from the point of view of information theory, they all lead to the increase of redundancy.

目前,民用数码相机上CMOS图像的压缩方法多数是在图像在插值后才进行图像压缩,压缩数据量是CFA的三倍,不利于空间相机图像压缩实时性。存在的RGGB型CFA的压缩方法,要么把两个绿色像素转换成一个,这降低了恢复图像的质量;要么针对Y矩阵设计复杂的结构变换或数值填充,算法复杂且仅适用于某一特定格式的RGGB型CFA。At present, most of the CMOS image compression methods on civilian digital cameras are image compression after image interpolation, and the amount of compressed data is three times that of CFA, which is not conducive to the real-time performance of space camera image compression. The existing RGGB-type CFA compression method either converts two green pixels into one, which reduces the quality of the restored image; or designs complex structural transformation or numerical filling for the Y matrix, and the algorithm is complex and only applicable to a specific format RGGB-type CFA.

基于此,本发明提供一种基于新型色彩空间转换的马赛克图像压缩方法。Based on this, the present invention provides a mosaic image compression method based on novel color space conversion.

发明内容Contents of the invention

本发明的目的在于针对上述现有技术恢复图像质量低、算法复杂且仅适用于某一特定格式的RGGB型CFA图像的问题,提供一种基于新型色彩空间转换的马赛克图像压缩方法,其实现简单,在编码码流位数相同的情况下能够带来更高的恢复质量,并且适用于任意格式的RGGB型CFA。The purpose of the present invention is to provide a mosaic image compression method based on a novel color space conversion for the problems of low image quality, complex algorithm and only applicable to a certain format of RGGB type CFA image in the prior art, which is simple to implement , which can bring higher recovery quality under the condition of the same number of coded stream bits, and is applicable to RGGB type CFA in any format.

为实现上述目的,本发明采用的技术方案为:To achieve the above object, the technical solution adopted in the present invention is:

基于新型色彩空间转换的马赛克图像压缩方法,包括以下步骤:A mosaic image compression method based on novel color space conversion, comprising the following steps:

步骤1、将RGGB型CFA图像中的G1、R、B、G2分量分别单独拼接构成一个通道,生成四通道模型;Step 1. The G 1 , R, B, and G 2 components in the RGGB-type CFA image are spliced separately to form a channel to generate a four-channel model;

步骤2、将步骤1中生成的四通道模型进行色彩空间转换,转换至YDgCoCg模型,得到亮度分量Y、色度分量Dg、Co、Cg;Step 2. Perform color space conversion on the four-channel model generated in step 1, and convert it to the YDgCoCg model to obtain the luminance component Y, chrominance components Dg, Co, and Cg;

步骤3、对步骤2中的色度分量Dg、Co、Cg进行DCT、JPEG色度量化,生成色度分量Dg′、Co′、Cg′;Step 3, carry out DCT, JPEG chromatic quantification to the chromaticity component Dg, Co, Cg in the step 2, generate chromaticity component Dg ', Co ', Cg ';

步骤4、对步骤3中色度分量Cg′进行反量化、IDCT生成色度分量Cg″,并进行数学优化,计算生成亮度分量

Figure GDA0003801451350000021
Step 4. Dequantize the chrominance component Cg′ in step 3, generate the chrominance component Cg″ by IDCT, and perform mathematical optimization to calculate and generate the luminance component
Figure GDA0003801451350000021

步骤5、对步骤4中亮度分量

Figure GDA0003801451350000022
进行DCT、JPEG亮度量化,生成亮度分量
Figure GDA0003801451350000023
Step 5, for the brightness component in step 4
Figure GDA0003801451350000022
Perform DCT and JPEG luminance quantization to generate luminance components
Figure GDA0003801451350000023

步骤6、对步骤3中色度分量Dg′、Co′、Cg′进行JPEG色度编码,对步骤5中亮度分量

Figure GDA0003801451350000024
进行JPEG亮度编码,并将码流发送;Step 6. Carry out JPEG chroma encoding to the chroma components Dg', Co', Cg' in the step 3, and carry out the chroma encoding in the step 5 to the luminance component
Figure GDA0003801451350000024
Perform JPEG brightness encoding and send the code stream;

步骤7、接收码流,进行反编码、反量化、IDCT、逆色彩空间转换,恢复CFA图像。Step 7: Receive the code stream, perform inverse coding, inverse quantization, IDCT, inverse color space conversion, and recover the CFA image.

进一步地,所述步骤2中色彩空间转换为:Further, the color space conversion in the step 2 is:

Figure GDA0003801451350000025
Figure GDA0003801451350000025

进一步地,所述步骤4中,数学优化为:

Figure GDA0003801451350000026
Further, in the step 4, the mathematical optimization is:
Figure GDA0003801451350000026

进一步地,所述步骤7具体包括:Further, the step 7 specifically includes:

S71、接收数据,并依次进行反编码、反量化、IDCT生成分量

Figure GDA0003801451350000027
Dg″、Co″、Cg″;S71. Receive data, and sequentially perform inverse encoding, inverse quantization, and IDCT to generate components
Figure GDA0003801451350000027
Dg", Co", Cg";

S72、对分量

Figure GDA0003801451350000028
Dg″、Co″、Cg″进行逆色彩空间转换,得到分量G1″、R″、B″′、G″2:S72, paired components
Figure GDA0003801451350000028
Dg″, Co″, Cg″ perform inverse color space conversion to obtain components G 1 ″, R″, B″′, G″ 2 :

Figure GDA0003801451350000029
Figure GDA0003801451350000029

S73、对分量G1″、R″、B″′、G″2进行与步骤1相反的操作,恢复得RGGB型CFA图像。S73. Perform the reverse operation on the components G 1 ″, R″, B″′, and G″ 2 as in step 1, and recover an RGGB-type CFA image.

本发明的工作原理在于:The working principle of the present invention is:

本发明提出四通道模型,通过色彩空间转换,将亮度编码的分量尺寸变为CFA尺寸的1/4,而色度编码产生的码流位数非常小,所以有效降低了编码码流的位数;The present invention proposes a four-channel model, through color space conversion, the component size of the luma code is changed to 1/4 of the CFA size, and the number of bits of the code stream generated by the chroma code is very small, so the number of bits of the code stream is effectively reduced ;

所述色彩空间转换方法为:The color space conversion method is:

Figure GDA0003801451350000031
Figure GDA0003801451350000031

其中,Y为亮度分量,Dg、Co、Cg为色度分量,Dg表示绿色差:Dg=G2-G1Among them, Y is the luminance component, Dg, Co, and Cg are the chrominance components, and Dg represents the green difference: Dg=G 2 -G 1 ;

假设对Y、Dg、Co、Cg分量分别进行DCT、量化、编码得Y′、Dg′、Co′、Cg′,则反编码、反量化、IDCT恢复得Y″、Dg″、Co″、Cg″,经过逆色彩空间转换:Assuming that Y, Dg, Co, and Cg components are respectively DCT, quantized, and encoded to obtain Y', Dg', Co', and Cg', then inverse encoding, inverse quantization, and IDCT restore Y", Dg", Co", and Cg ″, after inverse color space conversion:

Figure GDA0003801451350000032
Figure GDA0003801451350000032

此时相对于原CFA,产生的失真为:At this time, relative to the original CFA, the resulting distortion is:

Figure GDA0003801451350000033
Figure GDA0003801451350000033

为了使失真最小化:To minimize distortion:

Figure GDA0003801451350000034
Figure GDA0003801451350000034

找到一个奇点,且根据现实意义,这个点为最小值点:Find a singularity, and according to the practical sense, this point is the minimum point:

Figure GDA0003801451350000035
Figure GDA0003801451350000035

由此可见,压缩编码使得色度通道产生严重失真,导致解码端反量化得到的亮度分量更接近

Figure GDA0003801451350000036
(而不是Y)时,能够带来更小的整体失真,即更高的恢复质量;因此,本发明在编码端处理的亮度分量为
Figure GDA0003801451350000037
Figure GDA0003801451350000038
分量经过DCT、量化、编码得到
Figure GDA0003801451350000039
后,再经过反编码、反量化、ID CT得到的
Figure GDA00038014513500000310
Figure GDA00038014513500000311
仍然具有一定差距,但这个差距远远小于直接编码Y、解码生成的Y″与
Figure GDA00038014513500000312
的差距。It can be seen that compression coding causes severe distortion of the chroma channel, which leads to the brightness component obtained by inverse quantization at the decoding end closer to
Figure GDA0003801451350000036
(instead of Y), can bring smaller overall distortion, that is, higher restoration quality; therefore, the luminance component processed at the encoding end of the present invention is
Figure GDA0003801451350000037
Figure GDA0003801451350000038
The components are obtained through DCT, quantization, and encoding
Figure GDA0003801451350000039
After that, it is obtained by inverse encoding, inverse quantization, and ID CT
Figure GDA00038014513500000310
and
Figure GDA00038014513500000311
There is still a certain gap, but this gap is far smaller than the direct coding Y, the Y″ generated by decoding and the
Figure GDA00038014513500000312
difference.

综上所述,本发明的有益效果在于:In summary, the beneficial effects of the present invention are:

本发明提供一种基于新型色彩空间转换的马赛克图像压缩方法,将CFA的G1RBG2转换为四通道模型,然后使用新型色彩空间转换,消除像素之间的相关性,大大降低了编码码流的位数,使之能够直接通过传统编码框架进行编码;并且,本发明适用于任意格式的RGGB型CFA;同时,本发明中色彩空间转换矩阵和逆转换矩阵均为真实值,而不是近似结果,消除了转换矩阵带来的舍入误差;另外,本发明通过数学优化的方式更新需要编码的亮度分量,带来更好的恢复质量。The present invention provides a mosaic image compression method based on a new color space conversion, which converts G 1 RBG 2 of CFA into a four-channel model, and then uses a new color space conversion to eliminate the correlation between pixels and greatly reduce the encoding code stream The number of digits, so that it can be directly encoded by the traditional coding framework; and, the present invention is applicable to RGGB type CFA in any format; meanwhile, the color space transformation matrix and inverse transformation matrix in the present invention are real values, rather than approximate results , which eliminates the rounding error caused by the transformation matrix; in addition, the present invention updates the luminance component to be coded through mathematical optimization, resulting in better restoration quality.

附图说明Description of drawings

图1为本发明基于新型色彩空间转换的马赛克图像压缩方法的流程示意图。FIG. 1 is a schematic flow chart of the mosaic image compression method based on the novel color space conversion of the present invention.

图2为本发明实施例中Bayer格式CFA各个像素点颜色示意图。FIG. 2 is a schematic diagram of the color of each pixel of the Bayer format CFA in the embodiment of the present invention.

图3为本发明实施例中色彩空间转换示意图。FIG. 3 is a schematic diagram of color space conversion in an embodiment of the present invention.

具体实施方式Detailed ways

下面结合附图和实施例,对本发明作进一步详细描述。The present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments.

本实施例提供一种基于新型色彩空间转换的马赛克图像压缩方法,其流程如图1所示;本实施例以Bayer-CFA为例,具体包括以下步骤:This embodiment provides a mosaic image compression method based on novel color space conversion, and its process is shown in Figure 1; this embodiment takes Bayer-CFA as an example, and specifically includes the following steps:

步骤S1、将RGGB型CFA图像中的G1RBG2分量分别单独拼成一个通道,变为四通道模型,使每个分量单独组成矩阵,消除了CFA的复杂结构;Step S1, the G 1 RBG 2 components in the RGGB type CFA image are assembled into a channel separately, and become a four-channel model, so that each component forms a matrix separately, eliminating the complex structure of the CFA;

具体的,Bayer-CFA如图2所示,把Bayer-CFA分隔成2×2的像素块;如图3所示,挑选所有2×2像素块左上角的元素组合成G1分量,挑选所有2×2像素块右上角的元素组合成R分量,挑选所有2×2像素块左下角的元素组合成B分量,挑选所有2×2像素块右下角的元素组合成G2分量;Specifically, as shown in Figure 2, Bayer-CFA divides Bayer-CFA into 2×2 pixel blocks; as shown in Figure 3, select all elements in the upper left corner of 2×2 pixel blocks to form G1 components, and select all The elements in the upper right corner of the 2×2 pixel block are combined into the R component, the elements in the lower left corner of all the 2×2 pixel blocks are combined into the B component, and the elements in the lower right corner of all the 2×2 pixel blocks are combined into the G 2 component;

步骤S2、将四通道G1RBG2模型转到四通道YDgCoCg模型,如图3所示,消除了G1RBG2四个通道之间的相关性,降低了编码所需要的位数;Step S2, the four-channel G 1 RBG 2 model is transferred to the four-channel YDgCoCg model, as shown in Figure 3, the correlation between the four channels of G 1 RBG 2 is eliminated, and the number of bits required for encoding is reduced;

Figure GDA0003801451350000041
Figure GDA0003801451350000041

步骤S3、将S2中的Dg、Co、Cg分量进行DCT、JPEG色度量化,生成Dg′、Co′、Cg′;Step S3, carry out DCT, JPEG chroma quantification to Dg, Co, Cg component in S2, generate Dg', Co', Cg';

步骤S4、IDCT、反量化Cg′生成Cg″,数学优化,计算生成

Figure GDA0003801451350000051
恢复出的亮度分量更接近
Figure GDA0003801451350000052
将会带来更小的失真;Step S4, IDCT, inverse quantization Cg' to generate Cg", mathematical optimization, calculation generation
Figure GDA0003801451350000051
The restored luminance component is closer to
Figure GDA0003801451350000052
will result in less distortion;

具体的,IDCT、反量化Cg′生成Cg″,计算得到一个新的分量

Figure GDA0003801451350000053
Specifically, IDCT, inverse quantization Cg' generates Cg", and calculates a new component
Figure GDA0003801451350000053

Figure GDA0003801451350000054
Figure GDA0003801451350000054

步骤S5、对

Figure GDA0003801451350000055
进行DCT、JPEG亮度量化,生成
Figure GDA0003801451350000056
Step S5, to
Figure GDA0003801451350000055
Carry out DCT, JPEG brightness quantization, generate
Figure GDA0003801451350000056

步骤S6、对S3中生成的Dg′、Co′、Cg′进行JPEG色度编码,对S5中生产的

Figure GDA0003801451350000057
进行JPEG亮度编码,并将码流发送;Step S6, carry out JPEG chromaticity coding to Dg ', Co ', Cg ' that generate in S3, to the Dg ' that produces in S5
Figure GDA0003801451350000057
Perform JPEG brightness encoding and send the code stream;

步骤S7、接收码流,进行反编码、反量化、IDCT、逆色彩空间转换,恢复CFA图像;具体为:Step S7, receiving the code stream, performing inverse encoding, inverse quantization, IDCT, inverse color space conversion, and recovering the CFA image; specifically:

S71、接收数据,依次进行反编码、反量化、IDCT生成

Figure GDA0003801451350000058
Dg″、Co″、Cg″;S71. Receive data, perform inverse encoding, inverse quantization, and IDCT generation in sequence
Figure GDA0003801451350000058
Dg", Co", Cg";

S72、将

Figure GDA0003801451350000059
Dg″、Co″、Cg″进行逆色彩空间转换:S72. Will
Figure GDA0003801451350000059
Dg″, Co″, Cg″ perform inverse color space conversion:

Figure GDA00038014513500000510
Figure GDA00038014513500000510

S73、进行与S1相反的操作,恢复Bayer-CFA。S73. Perform an operation opposite to that of S1 to recover Bayer-CFA.

通过恢复CFA图像计算压缩图像的恢复质量,本发明具有更高恢复质量。本发明中,需要亮度编码的分量尺寸是CFA尺寸的1/4,所以编码码流的位数更少;且可以直接使用传统的编码框架直接编码,具有很高的鲁棒性;最后需要说明的是,本实施例中以Bayer格式CFA为例,但是本发明对任意RGGB型CFA同样有效。The restoration quality of the compressed image is calculated by restoring the CFA image, and the invention has higher restoration quality. In the present invention, the size of the component that requires luminance coding is 1/4 of the CFA size, so the number of bits in the coded stream is less; and the traditional coding framework can be directly used for direct coding, which has high robustness; finally, it needs to be explained Notably, the Bayer format CFA is taken as an example in this embodiment, but the present invention is also valid for any RGGB type CFA.

以上所述,仅为本发明的具体实施方式,本说明书中所公开的任一特征,除非特别叙述,均可被其他等效或具有类似目的的替代特征加以替换;所公开的所有特征、或所有方法或过程中的步骤,除了互相排斥的特征和/或步骤以外,均可以任何方式组合。The above is only a specific embodiment of the present invention. Any feature disclosed in this specification, unless specifically stated, can be replaced by other equivalent or alternative features with similar purposes; all the disclosed features, or All method or process steps may be combined in any way, except for mutually exclusive features and/or steps.

Claims (1)

1. The mosaic image compression method based on color space conversion comprises the following steps:
step 1, G in RGGB type CFA image 1 、R、B、G 2 The components are respectively and independently spliced to form a channel, and a four-channel model is generated;
step 2, performing color space conversion on the four-channel model generated in the step 1, and converting the four-channel model into an YDgCoCg model to obtain a brightness component Y, chrominance components Dg, co and Cg; the color space conversion is specifically:
Figure FDA0003843809960000011
step 3, performing DCT and JPEG chroma quantization on the chroma components Dg, co and Cg in the step 2 to generate chroma components Dg ', co ' and Cg ';
step 4, inverse quantization is carried out on the chrominance component Cg 'in the step 3, IDCT is carried out to generate chrominance component Cg', mathematical optimization is carried out, and the luminance component is generated through calculation
Figure FDA0003843809960000012
The mathematical optimization specifically comprises the following steps:
Figure FDA0003843809960000013
step 5, comparing the brightness component in step 4
Figure FDA0003843809960000014
DCT and JPEG brightness quantization are performed to generate brightness components
Figure FDA0003843809960000015
Step 6, JPEG chroma coding is carried out on the chroma components Dg ', co ' and Cg ' in the step 3, and the brightness component in the step 5 is subjected to
Figure FDA0003843809960000016
JPEG brightness coding is carried out, and the code stream is sent;
step 7, receiving the code stream, performing inverse coding, inverse quantization, IDCT and inverse color space conversion, and recovering the CFA image; the method comprises the following specific steps:
s71, receiving data, and sequentially performing inverse coding, inverse quantization and IDCT to generate components
Figure FDA0003843809960000017
Dg″、Co″、Cg″;
S72, component pair
Figure FDA0003843809960000018
Dg ', co ', cg ' are inverse color space converted to obtain a component G 1 ″、R″、B″′、G″ 2
Figure FDA0003843809960000019
S73, component G 1 ″、R″、B″′、G″ 2 And (4) performing the reverse operation of the step 1 to recover the RGGB type CFA image.
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