CN110971913B - Bayer image compression method based on filling Y channel - Google Patents

Bayer image compression method based on filling Y channel Download PDF

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CN110971913B
CN110971913B CN201911065295.XA CN201911065295A CN110971913B CN 110971913 B CN110971913 B CN 110971913B CN 201911065295 A CN201911065295 A CN 201911065295A CN 110971913 B CN110971913 B CN 110971913B
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yuv
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朱树元
贺康
刘光辉
曾兵
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University of Electronic Science and Technology of China
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/60—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding
    • H04N19/625—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding using discrete cosine transform [DCT]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
    • H04N19/119—Adaptive subdivision aspects, e.g. subdivision of a picture into rectangular or non-rectangular coding blocks
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    • H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
    • H04N19/12—Selection from among a plurality of transforms or standards, e.g. selection between discrete cosine transform [DCT] and sub-band transform or selection between H.263 and H.264
    • H04N19/122—Selection of transform size, e.g. 8x8 or 2x4x8 DCT; Selection of sub-band transforms of varying structure or type
    • H—ELECTRICITY
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    • H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
    • H04N19/124—Quantisation
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    • H04N19/186—Methods 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
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Abstract

The invention belongs to the technical field of image processing, and particularly provides a Bayer image compression method; the compression method for overcoming the defects of the prior Bayer image comprises the following steps: or converting two green pixels into one process reduces the quality of the restored image; or a complex structural transformation is designed for the Y matrix, the algorithm is complex and difficult to understand, and a decoder needs to be redesigned; or the pixel values in the Y matrix are rotated, translated and the like, so that the high-frequency components of a transform domain are increased, the number of bits required by coding is increased and the like. The method is simple and easy to implement, the bit rate can be effectively reduced under the condition that the speed is not influenced, and the picture quality is improved under the condition that the memory occupied by the picture is the same; and can be decoded directly using a JPEG decoder without the need to redesign the decoder.

Description

Bayer image compression method based on filling Y channel
Technical Field
The invention belongs to the technical field of image processing, and particularly relates to a Bayer image compression method.
Background
The camera is divided into a black-white camera and a color camera, the color camera is used for monitoring colored objects, the camera has a plurality of methods for obtaining color images, and the light-small-sized low-power-consumption CMOS camera obtains red, green and blue three spectral band images through a Bayer format optical filter array on a CMOS sensor and synthesizes the color images through interpolation. Most of the existing compression methods of CMOS images on civil digital cameras are image compression after image interpolation, and because a lot of redundant information is added to the images in the interpolation process, the amount of compressed data is three times of that of the original Bayer image, which is not beneficial to the real-time property of image compression.
In currently existing Bayer image compression methods: or converting two green pixels into one process reduces the quality of the restored image; or a complex structural transformation is designed for the Y matrix, the algorithm is complex and difficult to understand, and a decoder needs to be redesigned; or the pixel values in the Y matrix are rotated, translated, etc., increasing the high frequency components of the transform domain and increasing the number of bits required for encoding.
Disclosure of Invention
The invention aims to overcome the defects existing in the prior Bayer image compression method; providing a new Bayer image compression method; the method is simpler, does not increase the number of bits required for encoding, does not reduce the quality of the restored image, and does not require redesign of the decoder.
In order to achieve the purpose, the invention adopts the technical scheme that:
a Bayer image compression method comprising the steps of:
step 1: converting a Bayer-RGB matrix color space into a Bayer-YUV matrix color space;
step 2: separating a gray matrix Y and a chrominance matrix U, V from the Bayer-YUV matrix obtained in the step 1;
and step 3: dividing the gray matrix Y obtained in the step 2 into a plurality of 8 x 8 pixel blocks, and performing numerical filling on all the pixel blocks b to perform DCT:
the known two-dimensional DCT transform matrix form can be transformed into:
Vec(F)＝T*Vec(f)，
Figure BDA0002259131620000011
Figure BDA0002259131620000021
Figure BDA0002259131620000022
i,j＝0～7
wherein, C is a two-dimensional DCT coefficient matrix, Vec (F) represents the transformation domain of Vec (f), and Vec is a vectorization operator;
the operation specifically comprises the following steps:
step 3.1, vectorizing the pixel block b, which is called Vec (b);
step 3.2, selecting matrix T
(815162223242930313237383940444546474851525354555658596061626364) line,
Column (24689111315182022242527293134363840414345475052545657596163), the matrix of which is denoted as T11；
Step 3.3, selecting matrix T
(815162223242930313237383940444546474851525354555658596061626364) line,
Column (135710121416171921232628303233353739424446484951535558606264), the matrix of which is denoted as T10；
Step 3.4, the column vector composed of the inserted 32 values is:
Figure BDA0002259131620000023
wherein y is a column vector consisting of 32 effective pixels in the vector Vec (b);
the 32 interpolated numbers obtained in steps 3.5 and 3.3 are inserted into vector Vec (b) in sequence
(815162223242930313237383940444546474851525354555658596061626364) line, resulting in vector vec (f);
step 3.6, vec (f) ═ T vec (f) is performed,
Figure BDA0002259131620000024
the resulting vector vec (F) is reshaped into an 8 × 8 matrix F:
Figure BDA0002259131620000031
quantizing the matrix F by using a JPEG gray quantization table to obtain a quantization matrix
Figure BDA0002259131620000032
And 4, step 4: performing DCT (discrete cosine transformation) on the chrominance matrix U, V obtained in the step 2, and quantizing by using a chrominance quantization table to obtain a quantization matrix
Figure BDA0002259131620000033
And 5: for the quantization matrix obtained in step 3
Figure BDA0002259131620000034
And the quantization matrix obtained in step 4
Figure BDA0002259131620000035
JPEG entropy coding is carried out, and the data are transmitted;
step 6: receiving the data sent in the step 5, sending the data into a JPEG decoder to obtain a decoding matrix
Figure BDA0002259131620000036
Then, will
Figure BDA0002259131620000037
Splicing back to the Bayer-YUV matrix according to the reverse process of the step 2, and converting the Bayer-YUV matrix into the Bayer-RGB matrix according to the reverse process of the step 1.
The invention has the beneficial effects that:
the invention provides a Bayer image compression method based on a filling Y channel, which uses the information of all effective pixel points of a Y matrix in the process without losing any existing information; filling numerical values in the blank positions of the Y matrix, no more high-frequency components are generated in the frequency domain, and both positive and negative transformation can be operated by using matrix multiplication, so that the bit rate can be effectively reduced under the condition of not influencing the speed; the invention can directly use JPEG decoder to decode without redesigning decoder, and is simple and easy to operate.
Drawings
FIG. 1 is an overall flow chart of the Bayer image compression method of the invention.
Fig. 2 is a schematic diagram of a Bayer pattern picture in an embodiment of the present invention.
FIG. 3 is a schematic diagram of color space conversion according to an embodiment of the present invention.
FIG. 4 is a diagram illustrating color channel separation according to an embodiment of the present invention.
FIG. 5 is a diagram illustrating a method for calculating a padding value according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
This embodiment provides a Bayer image compression method, the flow of which is shown in fig. 1, where the Bayer pattern is composed of a plurality of Bayer units, each Bayer unit includes four pixels, which are two G components, one R component, and one B component, as shown in fig. 2: the method comprises the following steps:
step 1: two G components, one R component, and one B component in all Bayer cells are represented by the formula:
Figure BDA0002259131620000041
converting the Bayer-RGB matrix color space to a Bayer-YUV matrix color space, wherein Y, U, V is located at a position corresponding to G, R, B; as shown in fig. 3;
step 2: separating a Y channel, a U channel and a V channel from the Bayer-YUV matrix obtained in the step 1:
Y(2m-1 2n-1)＝S(2m-1 2n-1)
U(m n)＝S(2m-1 2n)
V(m n)＝S(2m 2n-1)
Y(2m 2n)＝S(2m 2n)
wherein S is a Bayer-YUV matrix, Y is a separated gray matrix, and U, V is a separated chrominance matrix; m and n represent the m-th row and the n-th column of the matrix; as shown in fig. 4;
and step 3: dividing the Y matrix obtained in step 2 into a plurality of 8 × 8 pixel blocks, then performing vacancy numerical filling on all the pixel blocks b, and performing DCT transformation, as shown in fig. 5:
the known two-dimensional DCT transform matrix form can be transformed into:
Vec(F)＝T*Vec(f)，
Figure BDA0002259131620000042
Figure BDA0002259131620000043
Figure BDA0002259131620000044
i,j＝0～7
wherein C is a two-dimensional DCT transform coefficient matrix,
Figure BDA0002259131620000045
representing the Kronecker product, Vec (f) representing the transform domain of Vec (f), Vec being the vectorization operator;
the operation specifically comprises the following steps:
step 3.1, vectorizing the pixel block b, which is called Vec (b);
step 3.2, selecting matrix T
(815162223242930313237383940444546474851525354555658596061626364) line,
Column (24689111315182022242527293134363840414345475052545657596163), the matrix of which is denoted as T11；
Step 3.3, selecting matrix T
(815162223242930313237383940444546474851525354555658596061626364) line,
Column (135710121416171921232628303233353739424446484951535558606264), the matrix of which is denoted as T10；
Step 3.4, the column vector composed of the inserted 32 values is:
Figure BDA0002259131620000051
wherein y is a column vector consisting of 32 effective pixels in the vector Vec (b);
the 32 interpolated numbers obtained in steps 3.5 and 3.3 are inserted into vector Vec (b) in sequence
(815162223242930313237383940444546474851525354555658596061626364) line, resulting in vector vec (f);
step 3.6, vec (f) ═ T vec (f) is performed,
Figure BDA0002259131620000052
the resulting vector vec (F) is reshaped into an 8 × 8 matrix F:
Figure BDA0002259131620000053
quantizing the matrix F by using a JPEG gray quantization table to obtain a quantization matrix
Figure BDA0002259131620000054
And 4, step 4: performing DCT transformation on the U matrix and the V matrix obtained in the step 2, and quantizing by using a chrominance quantization table to obtain a quantization matrix
Figure BDA0002259131620000055
And 5: for the quantization matrix obtained in step 3
Figure BDA0002259131620000056
And the quantization matrix obtained in step 4
Figure BDA0002259131620000057
JPEG entropy coding is carried out, and the data are transmitted;
step 6: receiving the data sent in the step 5, sending the data into a JPEG decoder to obtain a decoding matrix
Figure BDA0002259131620000061
Then will be
Figure BDA0002259131620000062
Splicing back to the Bayer-YUV matrix according to the reverse process of the step 2:
Figure BDA0002259131620000063
Figure BDA0002259131620000064
Figure BDA0002259131620000065
Figure BDA0002259131620000066
the Bayer-YUV matrix is then converted to a Bayer-RGB matrix following the reverse procedure of step 1:
Figure BDA0002259131620000067
by adopting the Bayer image compression method, the restored image has better quality, and has smaller bit rate after compression without redesigning a decoder.
While the invention has been described with reference to specific embodiments, any feature disclosed in this specification may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise; all of the disclosed features, or all of the method or process steps, may be combined in any combination, except mutually exclusive features and/or steps.

Claims (1)

1. A Bayer image compression method based on filling Y channels comprises the following steps:
step 1: converting a Bayer-RGB matrix color space into a Bayer-YUV matrix color space;
step 2: separating a gray matrix Y and a chrominance matrix U, V from the Bayer-YUV matrix obtained in the step 1;
and step 3: dividing the gray matrix Y obtained in the step 2 into a plurality of 8 x 8 pixel blocks, performing numerical filling on all the pixel blocks b, and performing DCT (discrete cosine transformation):
the two-dimensional DCT transform matrix is known in the form:
Vec(F)＝T*Vec(f)，
Figure FDA0003157889590000011
wherein, Vec (F) represents the transform domain of Vec (f), Vec is the vectorization operator; c is a two-dimensional DCT transform coefficient matrix:
Figure FDA0003157889590000012
Figure FDA0003157889590000013
i,j＝0～7
the DCT transform specifically includes:
step 3.1, vectorizing the pixel block b, which is called Vec (b);
step 3.2, calculating T matrix, selecting matrix T
(815162223242930313237383940444546474851525354555658596061626364) line,
Column (24689111315182022242527293134363840414345475052545657596163), the matrix of which is denoted as T11；
Step 3.3, selecting matrix T
(815162223242930313237383940444546474851525354555658596061626364) line,
Column (135710121416171921232628303233353739424446484951535558606264), the matrix of which is denoted as T10；
Step 3.4, the column vector composed of 32 values to be inserted is:
Figure FDA0003157889590000014
wherein y is a column vector consisting of 32 effective pixels in the vector Vec (b);
the 32 values in the column vector x obtained in step 3.5 and step 3.4 are inserted into the vector Vec (b) in sequence
Line (815162223242930313237383940444546474851525354555658596061626364), generating vec (f);
step 3.6, perform vec (F) ═ T vec (F), and transform the resulting vector vec (F) into an 8 × 8 matrix F:
Figure FDA0003157889590000021
quantizing the matrix F by using a JPEG gray quantization table to obtain a quantization matrix
Figure FDA0003157889590000022
And 4, step 4: performing DCT (discrete cosine transformation) on the chrominance matrix U, V obtained in the step 2, and quantizing by using a chrominance quantization table to obtain a quantization matrix
Figure FDA0003157889590000023
And 5: for the quantization matrix obtained in step 3
Figure FDA0003157889590000024
And step 4The quantization matrix obtained in
Figure FDA0003157889590000025
JPEG entropy coding is carried out, and the data are transmitted;
step 6: receiving the data sent in the step 5, sending the data into a JPEG decoder to obtain a decoding matrix
Figure FDA0003157889590000026
Then, will
Figure FDA0003157889590000027
Splicing back to the Bayer-YUV matrix according to the reverse process of the step 2, and converting the Bayer-YUV matrix into the Bayer-RGB matrix according to the reverse process of the step 1.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101977330A (en) * 2010-11-12 2011-02-16 北京空间机电研究所 Bayer image compression method based on YUV conversion
CN103414903A (en) * 2013-08-28 2013-11-27 清华大学 Compressing method and device for Bayer format images
CN104427349A (en) * 2013-08-20 2015-03-18 清华大学 Bayer image compression method
JP2018006999A (en) * 2016-06-30 2018-01-11 キヤノン株式会社 Image encoding device and control method therefor

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8428120B2 (en) * 2007-12-11 2013-04-23 Taiwan Imagingtek Corporation Method and apparatus of Bayer pattern direct video compression

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101977330A (en) * 2010-11-12 2011-02-16 北京空间机电研究所 Bayer image compression method based on YUV conversion
CN104427349A (en) * 2013-08-20 2015-03-18 清华大学 Bayer image compression method
CN103414903A (en) * 2013-08-28 2013-11-27 清华大学 Compressing method and device for Bayer format images
JP2018006999A (en) * 2016-06-30 2018-01-11 キヤノン株式会社 Image encoding device and control method therefor

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