CN102271251A - Lossless Image Compression Method - Google Patents

Abstract

The invention discloses a distortion-free image compression method which is used for processing an image with a plurality of pixels, wherein the image width of the image is W. The undistorted image compression method comprises the steps of selecting continuous N pixels from an image, wherein N is a positive integer greater than or equal to 2, and is less than the width W of the image; executing a difference pulse code modulation means to obtain N non-negative difference values corresponding to the N pixels according to the values of the N pixels; calculating to obtain coding parameters according to the N nonnegative difference values; and coding the N nonnegative difference values according to the coding parameters.

Description

Lossless Image Compression Method

technical field

The invention relates to a distortion-free image compression method, in particular to a distortion-free image compression method requiring less memory space.

Background technique

With the advancement of electronics and information technology, the development of technologies for processing and displaying images on computers or various electronic devices is becoming more and more common. Early electronic information technology could only store or process digital images with relatively low pixels. However, people's demand for high-quality images is increasing, and how to process and store high-quality images has become a very hot focus. In order to obtain high-quality images, a lossless compression method is often used in image compression.

However, a high-quality image without distortion needs to store relatively high pixels, which also poses a big challenge to the calculation speed of the calculator and the capacity of the storage medium. For example, in the lossless compression algorithm, the joint photographic experts group Lossless (JPEG-LS) uses prediction and context modeling to achieve a better compression ratio. The aforementioned mode requires at least one entire row of pixels of the image to be predicted in various modes, thus requiring a large amount of computing and storage space, which increases the cost of the encoder. Moreover, for applications that do not require a high compression rate, JPEG-LS still has high computational complexity, which increases the burden on the encoder.

In addition, users have higher and higher requirements for image resolution, which also increases the burden of image compression. If the existing lossless compression method is used, once the resolution of the image is increased, it will take a long time and a large amount of storage space for the encoder to process it into a high-quality image. In other words, the existing lossless compression methods have the problems of high computational complexity resulting in low computational efficiency and huge temporary storage space required.

Therefore, how to design a lossless compression method that can take into account the requirements of high visual quality, low computational complexity and low temporary storage space is very important in the industry.

Contents of the invention

In order to solve the above problems, the present invention provides a lossless (lossless, also referred to as lossless) image compression method. The lossless image compression method requires less memory space and can process an image with multiple pixels at a faster speed. An image width of the image is W. The image compression method without distortion comprises the following steps: (a) select consecutive N pixels from the image, wherein N is a positive integer greater than or equal to 2, and N is less than the image width W; (b) execute a difference pulse code Modulation (Differential pulse code modulation, DPCM) means to obtain N non-negative difference values corresponding to the N pixels based on the values of the N pixels; (c) calculate a code based on the N non-negative difference values A parameter (coding parameter); and (d) encoding the N non-negative difference values according to the encoding parameter.

In order to compress the entire image, the image compression method without distortion may further include the following steps: (e) continuously select the next N consecutive pixels of the image; and (f) return to step (b) until all pixels of the image are compressed .

In step (a), a buffer may directly receive the selected N pixels in the image.

And the difference pulse code modulation means of step (b) can comprise: according to the value of these N pixels, obtain N pixel difference values corresponding to these N pixels respectively; And carry out a conversion to this N pixel difference values respectively ( mapping) to obtain N non-negative difference values respectively corresponding to the N pixel difference values.

According to an embodiment of the present invention, according to the values of the N pixels, the step of obtaining N pixel difference values respectively corresponding to the N pixels may include: the value _P of the first pixel in the N pixels as the first pixel difference value d ₀ ; and respectively calculating the difference between the value P _i of the i-th pixel in the N pixels and the value P _i-1 of the previous pixel, as the other N-1 pixel difference values, Wherein i is a positive integer, and 0<i<N.

The transformation means may include: when the i-th pixel difference d _i among the N pixel differences is greater than or equal to zero, the corresponding i-th non-negative difference n _i is the pixel difference d _i multiplied by 2, wherein 0=<i<N; and when the i-th pixel difference d _i among the N pixel differences is less than zero, the corresponding i-th non-negative number difference n _i is the pixel difference d _i multiplied by 2 and then minus 1.

其中n_i为此N个非负数差值中的第i个非负数差值。The encoding parameters used to encode the N non-negative difference values can be

Where n _i is the i-th non-negative difference value among the N non-negative difference values.

The above step (d) may include: encoding the N non-negative difference values with Golomb-Rice code according to the encoding parameters.

To sum up, according to the distortion-free image compression method of the present invention, N pixels in the image are selected, and the values of the N pixels are encoded by variable length coding. No other pixels in the image are needed to process the N pixels, so the encoder's buffer does not need to save the values of other pixels. Therefore, the image compression method without distortion has the advantages of saving a large amount of temporary storage space, and the compression method is simple and efficient.

Description of drawings

FIG. 1 is a schematic diagram of an image according to an embodiment of the present invention;

FIG. 2 is a flowchart of a method for image compression without distortion according to an embodiment of the present invention;

FIG. 3 is a flowchart of a differential pulse code modulation method according to an embodiment of the present invention;

FIG. 4 is a flowchart of step S41 according to an embodiment of the present invention;

Fig. 5 is the flowchart of the conversion means according to an embodiment of the present invention; And

FIG. 6 is a flowchart of a method for image compression without distortion according to another embodiment of the present invention.

Among them, reference signs:

20 images

22 processing window

Detailed ways

The detailed features and advantages of the present invention are described in detail below in the embodiments, the content of which is sufficient to enable any person skilled in the art to understand the technical content of the present invention and implement it accordingly, and according to the disclosed content, claims and accompanying drawings of this specification , any person skilled in the art can easily understand the related objects and advantages of the present invention.

The present invention provides a lossless (also called lossless) image compression method for processing an image with a plurality of pixels to compress the image. The lossless image compression method can be implemented in an encoder.

Please refer to FIG. 1 and FIG. 2 , which are respectively a schematic diagram of an image and a flow chart of a distortion-free image compression method according to an embodiment of the present invention.

The image 20 has W×L pixels, where W is an image width of the image 20 and L is an image length. For example, the image 20 has 640×480 pixels or 128×128 pixels; then the image width W is 640 (pixels) or 128 (pixels). Since pixels in the same row in the image 20 have a strong correlation with each other, it can be exploited for image compression.

When performing the image compression method without distortion, first select N consecutive pixels from the image 20 (step S30); wherein N is a positive integer greater than or equal to 2, and N is smaller than the image width W. In order to implement the image compression method without distortion more efficiently, a value that can divide the image width W by an integer can be taken as N. In other words, N can divide the image width W of the image 20 evenly. For example, when the image width W is 128 (pixels), N can be taken as 16. The step S30 of selecting consecutive N pixels can be implemented with a processing window 22 . The length of the processing window 22 is N, and the N pixels framed by the processing window 22 are the N pixels compressed according to the following process.

The encoder has a buffer for temporarily storing the part of the image 20 that needs to be processed in the buffer. In more detail, the image 20 can be a file stored in memory, or can be image data transmitted by a photosensitive unit of an image capture device. In step S30, the consecutive N pixels can be selected from the image 20 received by the memory or the photosensitive unit, and the values P ₀ -P _N-1 of the N pixels can be directly received by the register.

Therefore, when the image 20 is compressed by the lossless image compression method, only N pixels of the processing window 22 need to be used, and other pixels in the image 20 do not need to be used. Therefore, it is only necessary to store the N pixels in the buffer of the encoder, instead of storing the entire row of W pixels in the image 20 .

After selecting consecutive N pixels, perform a Differential Pulse Code Modulation (DPCM) method on the N pixels; to obtain the corresponding N pixels according to the values P ₀ to P _N-1 of the N pixels N non-negative difference values n ₀ -n _N-1 of pixels (step S40 ). The value of the pixel can be, for example, gray scale value, three primary color values (RGB value), or hue, saturation, and lightness of HSL color space. Lossless image compression methods do not place restrictions on the content of pixel values.

Referring to FIG. 3 , it is a flowchart of a differential pulse code modulation method according to an embodiment of the present invention.

The distortion-free image compression method utilizes differential pulse code modulation to obtain N non-negative difference values n ₀ ˜n _N-1 corresponding to the processing window 22 to perform distortion-free compression. The differential pulse code modulation means can obtain N pixel difference values d ₀ ˜d _N-1 respectively corresponding to the N pixels according to the values P ₀ ˜P _N-1 of the N pixels (step S41 ). Next, a mapping means is performed on the N pixel difference values to obtain N non-negative difference values n ₀ -n _N−1 respectively corresponding to the N pixel difference values (step S46 ).

Please cooperate with FIG. 3 and refer to FIG. 4 and FIG. 5 , which are respectively a flow chart of step S41 and a flow chart of the transformation means in step S46 according to an embodiment of the present invention.

In step S41, the pixel difference values d ₀ -d _N-1 can be calculated according to the following formulas 1 and 2.

d ₀ ＝P ₀ Formula 1

d _i ＝P _i -P _i-1 , 0<i<N, and i is a positive integer Formula 2

Where P ₀ is the value of the first pixel among the N pixels in the processing window 22, and P _i is the value of the i-th pixel among the N pixels in the processing window 22. And d ₀ is the pixel difference corresponding to P ₀ , and d _i is the pixel difference corresponding to P _i .

Formula 1 uses the value P ₀ of the first pixel among the N pixels as the first pixel difference value d ₀ (step S42). Equation 2 respectively calculates the difference between the value P _i of the i-th pixel among the N pixels and the value P _i-1 of the previous pixel, as the difference values d ₁ to d _N-1 of the other N-1 pixels, where 0 <i<N (step S43). In other words, the pixel difference values d ₁ -d _N-1 are obtained by subtracting the values P ₀ -P _N-1 of adjacent pixels in pairs.

According to another embodiment of the present invention, the value P ₋₁ of the pixel preceding the first pixel of the processing window 22 in the image 20 may be stored. The first pixel difference _d0 corresponding to the processing window 22 is the difference between _P0 and P ₋₁ .

After obtaining the pixel difference values d ₀ to d _N-1 corresponding to the processing window 22 in step S41, the conversion means can first judge whether these pixel difference values d _i are greater than or equal to 0 (step S47), and then according to the following formula 3 and Formula 4 calculates non-negative difference values n ₀ to n _N-1 .

n _i =2×d _i , if d _i ≥0 Equation 3

n _i ＝2×d _i -1, if d _i ＜0 Formula 4

Where n _i is the value of the i-th pixel among the N non-negative difference values n ₀ ˜n _N-1 , and 0≤i<N.

For the pixel difference d _i greater than or equal to 0, Equation 3 multiplies the pixel difference d _i by 2 as the non-negative difference n _i . That is to say, the non-negative difference value n _i is the pixel difference value d _i multiplied by 2 (step S48 ). As for the pixel difference d _i less than 0, Equation 4 multiplies the pixel difference d _i by 2 and then subtracts 1 as the non-negative difference n _i . That is to say, the non-negative difference value n _i is the pixel difference value d _i multiplied by 2 and then subtracted by 1 (step S49 ).

In this way, N non-negative difference values n ₀ ˜n _N-1 corresponding to the N pixels can be obtained in step S40 according to the values P ₀ ˜P N _-1 of the N pixels.

Please go back to Figure 2. Then, according to the N non-negative difference values n ₀ ~n _N-1 , a coding parameter (coding parameter) k is calculated (step S50); and according to the coding parameter k, the N non-negative difference values n ₀ ~n _N-1 is encoded (step S60).

The step S60 may include: encoding the N non-negative difference values n ₀ ˜n _N−1 by Golomb-Rice code according to the encoding parameter k.

The encoding parameters used to process the N non-negative difference values can be

The Columbus-Rice code is a variable-length code (variable-length code, VLC), which encodes a value with a higher probability of occurrence to a shorter code. According to Columbus-Rice coding, a divisor m is first set according to the coding parameter k. Then divide the N non-negative difference values n ₀ to n _N-1 by the divisor m to obtain the corresponding N quotients Q and remainder R. The divisor m is 2 to the kth power (2 ^k ).

The Columbus-Rice encoding then encodes the obtained quotient Q into a unary code, and encodes the quotient R into a k-bit binary code.

For example, assume that the non-negative difference value n ₀ is 163, and the encoding parameter k is 5. Therefore, it can be calculated that the quotient Q of the non-negative difference n ₀ is 5, and the remainder R is 3. Then the quotient Q of the non-negative difference value n ₀ can be coded as 111110 in unary code, and the remainder R can be coded as 00011 in binary code.

In this way, the values P ₀ ˜P _N−1 of the N pixels in the processing window 22 can be compressed into a distortion-free variable-length code. Instead, the entire image 20 can be compressed by a lossless image compression method. Please refer to FIG. 6 , which is a flowchart of a lossless image compression method according to another embodiment of the present invention.

After performing step S30 to step S60 to compress the N pixels of the processing window 22, it may also be determined whether all the pixels of the image 20 have been compressed (step S70). If the image 20 has not been fully compressed, continue to select the next N consecutive pixels of the image 20 (step S80), and process the N pixels selected in step S80 with steps S40, S50 and S60. If the image 20 has been compressed into a compressed image file, the process can end.

That is to say, in the image compression method without distortion, another N pixels are selected after the N pixels selected in step S30 , and the other N pixels selected can be used as a new processing window 22 for processing.

To sum up, according to the distortion-free image compression method of the present invention, N pixels in the image are selected, and the values of the selected pixels are encoded by variable length coding. Since other pixels in the image do not need to be used during compression, the buffer of the encoder only needs to store the values of the N pixels, which can save a lot of temporary storage space. Moreover, the image compression method without distortion only processes N pixel values in each image window one by one to compress the entire image, and has the advantage of simple and efficient compression method.

Claims (8)

1. undistorted method for compressing image, in order to the image that processing has a plurality of pixels, wherein a picture traverse of this image is W, it is characterized in that, this undistorted method for compressing image comprises:

A, by choosing continuous N this pixel in this image, wherein N is the positive integer more than or equal to 2, and N is less than this picture traverse W;

B, carry out difference pulse code modulated means,, obtain N nonnegative number difference that should N pixel with value according to this N pixel;

C, according to this N nonnegative number difference, calculate a coding parameter; And

D, according to this coding parameter, this N nonnegative number difference encoded.

2. undistorted method for compressing image as claimed in claim 1 is characterized in that other comprises:

E, following N this continuous pixel that continues and choose this image; And

F, return step b, up to all these pixels of having compressed this image.

3. undistorted method for compressing image as claimed in claim 1 is characterized in that, these difference pulse code modulated means comprise:

According to the value of this N pixel, obtain corresponding respectively to N pixel value difference of this N pixel; And

Respectively this N pixel value difference is carried out a shift means, to obtain corresponding respectively to this N nonnegative number difference of this N pixel value difference.

4. undistorted method for compressing image as claimed in claim 3 is characterized in that, this is according to the value of this N pixel, and the step that obtains corresponding respectively to N pixel value difference of this N pixel comprises:

Value P with first this pixel in this N pixel ₀As first this pixel value difference d ₀And

Calculate the value P of i this pixel in this N pixel respectively _iThe value P of this pixel previous with it _I-1Poor, as other N-1 this pixel value difference, wherein i is a positive integer, and 0＜i＜N.

5. undistorted method for compressing image as claimed in claim 3 is characterized in that, this shift means comprises:

I in this N pixel value difference this pixel value difference d _iMore than or equal to zero the time, corresponding i this nonnegative number difference n _iBe this pixel value difference d _iMultiply by 2, wherein 0=＜i＜N; And

I in this N pixel value difference this pixel value difference d _iLess than zero the time, corresponding i this nonnegative number difference n _iBe this pixel value difference d _iMultiply by 2 and subtract 1 again.

6. undistorted method for compressing image as claimed in claim 1 is characterized in that this coding parameter is

N wherein _iBe the i in this N the nonnegative number difference this nonnegative number difference.

7. undistorted method for compressing image as claimed in claim 1 is characterized in that, this steps d comprises:

According to this coding parameter, this N nonnegative number difference encoded with Columbus-Lai Si coding.

8. undistorted method for compressing image as claimed in claim 1 is characterized in that, this step a is directly received this N pixel that is selected in this image by a buffer.

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