CN110099279B - Method for adjusting lossy compression based on hardware - Google Patents
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- CN110099279B CN110099279B CN201810093981.7A CN201810093981A CN110099279B CN 110099279 B CN110099279 B CN 110099279B CN 201810093981 A CN201810093981 A CN 201810093981A CN 110099279 B CN110099279 B CN 110099279B
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Abstract
The application discloses a method for adjusting lossy compression based on hardware, which adopts the technical scheme that: partitioning an input image, and calculating the pixel mean value and the pixel range of each macro block; when the pixel range is larger than a set threshold value, quantizing each macro block to obtain a macro block transformation result parameter list; when the pixel range is not larger than the set threshold, setting a macro block transformation result parameter list according to the pixel mean value; and obtaining an image compression parameter list according to the macro block transformation result parameter list, and performing write compression on the image compression parameter list to obtain a compressed image file. By adopting the technical scheme of the invention, the calculated amount can be greatly reduced, and the calculation can be conveniently realized in a hardware environment only by using a small amount of addition, subtraction and comparison operations; the invention provides adjustable parameters such as a transformation threshold value and the like, can more flexibly select the optimal result suitable for the current requirement, and is suitable for various different compression requirements.
Description
Technical Field
The invention relates to the field of image processing, in particular to a method for adjustable lossy compression based on hardware.
Background
When images or videos are transmitted, due to the limitations of transmission bandwidth and transmission rate, the transmitted videos or images need to be compressed. Due to the huge amount of original sampling data, the limited storage capacity of a memory, the limitation of factors such as bandwidth and transmission rate during data transmission, the lossy compression of the image or video file with high compression ratio is imperative.
In order to meet the requirements of different compression ratios and image quality and to make it easier for an algorithm to be implemented on a hardware level (for example, the conventional commonly-used jpeg lossy compression method has too high computational complexity and is difficult to implement on the basis of hardware), it is of great practical significance to research an adjustable lossy compression method based on hardware.
Disclosure of Invention
In order to make image compression more satisfactory to use requirements, the application provides a method of adjustable lossy compression based on hardware.
The technical scheme adopted by the application is as follows: a hardware-based compression method for adjustable lossy compression, comprising:
partitioning an input image, and calculating the pixel mean value and the pixel range of each macro block;
when the pixel range is larger than a set threshold value, quantizing each macro block to obtain a macro block transformation result parameter list; when the pixel range is not larger than the set threshold, setting a macro block transformation result parameter list according to the pixel mean value;
and obtaining an image compression parameter list according to the macro block transformation result parameter list, and performing write compression on the image compression parameter list to obtain a compressed image file.
The interactive interface displays various preset values for a user to select, and the user selects one preset value as a set threshold according to the requirement of picture quality.
The quantizing each macro block to obtain a conversion result parameter list of each macro block specifically comprises the following substeps:
calculating a quantization threshold and a quantization scale list;
obtaining a quantized macro block pixel deviation matrix according to the quantization scale list;
and setting a macroblock transformation result parameter list according to the quantized macroblock pixel deviation matrix, the pixel mean value of each macroblock, the quantization identification and the quantization threshold.
The calculating of the quantization threshold specifically includes calculating a difference between a maximum value of a pixel of the current macroblock and a mean value of the pixel of the current macroblock, calculating a difference between a mean value of the pixel of the current macroblock and a minimum value of the pixel of the current macroblock, comparing the maximum values of the two difference values, and taking the maximum value of the two difference values as the quantization threshold of the current macroblock.
And calculating a quantization scale list, specifically setting specific ranges of a plurality of quantization intervals of the current macro block.
The obtaining of the quantized macroblock pixel deviation matrix according to the quantization scale list specifically includes: and setting endpoints of the multiple quantization intervals as a preset value group, sequentially determining corresponding values of pixel deviation values in the current macro block in the preset value group as the quantized pixel deviations of all positions of the current macro block, and combining the quantized pixel deviations of all positions of the current macro block to obtain a macro block pixel deviation matrix.
The macroblock transform result parameter list includes a pixel mean, a quantization flag, a quantization threshold of an effective quantization macroblock, and a quantized macroblock pixel deviation matrix.
The obtaining of the image compression parameter list according to the macroblock conversion result parameter list specifically includes:
summarizing to obtain an image transformation result parameter list according to the macro block transformation result parameter list;
and calculating an image compression parameter list according to the image transformation result parameter list.
The image compression parameter list comprises a pixel mean reference value, a quantization threshold reference value, a total number of effective quantization macro blocks, a coding bit width list and a coding information sequence.
A hardware-based decompression method of adjustable lossy compression, comprising:
decompressing according to the compressed image file to obtain an image compression parameter list; decoding the image compression parameter list to obtain a conversion result parameter list of each macro block;
when the pixel range in the transformation result parameter list of each macro block is greater than a set threshold value, restoring each pixel value of the current macro block into the pixel mean value of the current macro block plus a corresponding deviation value; when the pixel range in the transformation result parameter list of each macro block is not larger than a set threshold value, restoring each pixel value of the current macro block into the pixel average value of the current macro block;
and summarizing the pixel values of the restored macro blocks to obtain a decompressed image and outputting the decompressed image.
The decoding of the image compression parameter list to obtain a conversion result parameter list of each macro block specifically includes:
obtaining the pixel mean value of each macro block according to the pixel mean value reference value and the mean value sequence in the coding information sequence;
obtaining the quantization identification of each macro block according to the total number of the effective quantization macro blocks and the identification sequence in the coding information sequence;
obtaining the quantization threshold of each effective quantization macro block according to the quantization threshold reference value and the threshold sequence in the coding information sequence;
obtaining a quantized deviation value matrix according to the quantization threshold value and the difference value sequence in the coding information sequence;
and combining the obtained pixel mean value of each macro block, the quantization identification, the quantization threshold of the effective quantization macro block and the quantized deviation value matrix to obtain a transformation result parameter list of each macro block.
The beneficial effects obtained by the invention are as follows: by adopting the technical scheme of the invention, the calculated amount can be greatly reduced, and the calculation can be conveniently realized in a hardware environment only by using a small amount of addition, subtraction and comparison operations; the invention provides adjustable parameters such as a transformation threshold value and the like, can more flexibly select the optimal result suitable for the current requirement, and is suitable for various different compression requirements.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art according to the drawings.
FIG. 1 is a flow chart of a prior art compression method;
fig. 2 is a flowchart of a compression method for hardware-based adjustable lossy compression according to an embodiment of the present invention;
FIG. 3 is a flow chart of a prior art decompression method;
fig. 4 is a flowchart of a decompression method for hardware-based adjustable lossy compression according to a second embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application are clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Example one
Referring to fig. 1, the conventional compression method specifically includes:
step 101: dividing an input image into blocks, and transforming each macro block to obtain an image transformation result parameter;
step 102: summarizing and sorting the image transformation result parameters to obtain an image compression parameter list;
step 103: coding, writing and compressing the image according to the image compression parameter list to obtain a compressed image file;
the first embodiment of the present invention is an improvement on the basis of the existing compression method to realize adjustable lossy compression, and the improved compression method specifically comprises:
a compression method of adjustable lossy compression based on hardware, as shown in fig. 2, the method comprising:
step 201: partitioning an input image, calculating and storing the pixel mean value and the pixel range of each macro block;
step 202: judging whether the pixel range is larger than a set threshold value, if so, executing a step 203, otherwise, setting a macro block transformation result parameter list according to the pixel mean value, and executing a step 206;
the set threshold is a corresponding preset value selected by a user according to the requirement of the picture quality, preferably, a user interaction interface displays various preset values for the user to select, and the user selects one of the preset values as the set threshold according to the use requirement of the loss degree of compression.
Step 203: calculating a quantization threshold and a quantization scale list;
the method for calculating the quantization threshold Theta of each macro block is as follows: calculating the difference value between the maximum value of the current macro block pixel and the average value of the current macro block pixel, calculating the difference value between the average value of the current macro block pixel and the minimum value of the current macro block pixel, comparing the maximum values of the two difference values, and taking the maximum value of the two difference values as the quantization threshold value of the current macro block;
the calculation method of the quantization scale list specifically comprises the following steps: setting a specific range of multiple quantization intervals of the current macroblock, preferably setting 6 values including-Theta, -Theta x 0.75, -Theta x 0.25, Theta x 0.75, Theta, for example, if the calculated quantization threshold is 10, the quantization scale list is { -10, -7.5, -2.5,2.5,7.5,10 }.
Step 204: obtaining a quantized macro block pixel deviation matrix according to the quantization scale list;
setting endpoints of a plurality of quantization intervals as a preset value group, sequentially determining corresponding values of pixel deviation values in a current macro block in the preset value group as quantization pixel deviations of all positions of the current macro block, and combining the quantization pixel deviations of all the positions of the current macro block to obtain a macro block pixel deviation matrix;
for example,
a value in the range-Theta to-Theta 0.75 is set to a first value, for example-2;
setting a value in the range-Theta 0.75 to-Theta 0.25 to a second value, for example-1;
setting a value in the range-Theta 0.25 to a third value, e.g. 0;
setting a value in the range Theta 0.25 to Theta 0.75 to a fourth value, for example 1;
setting a value in the range of Theta 0.75 to Theta to a fifth value, for example 2;
comparing the deviation value of each pixel in the current macro block within the value range, and setting the deviation value as corresponding first to fifth values; for example, the pixel values of the current macroblock are-9, -1,3 … …, the pixel deviations of the current macroblock after quantization are { -2,0,1 … … }, and all the quantized pixel deviations of the current macroblock are combined to obtain a macroblock pixel deviation matrix;
through the quantization compression, the pixel value of the pixel in the macro block which greatly exceeds the use requirement of a user is compressed from-10 to-2 to 2, so that the memory space is greatly saved, and the transmission rate of the picture data is improved.
Step 205: setting a macroblock transformation result parameter list according to the quantization threshold, the pixel mean and the quantized macroblock pixel deviation matrix;
the macro block transformation parameter list information comprises a pixel mean value, a quantization identification, a quantization threshold value of an effective quantization macro block and a quantized macro block pixel deviation matrix;
the quantization flag specifically indicates whether each macroblock is quantized, preferably, the macroblock to be quantized is set to 1, and the macroblock not to be quantized is set to 0, for example, an input image includes four macroblocks, where macroblock 1 and macroblock 3 are quantized, and macroblock 2 and macroblock 4 are not quantized, and the quantization flag is 1010;
if the pixel range of a certain macro block of the image is smaller than a set threshold, quantization is not needed, and a quantized macro block pixel deviation matrix in the macro block transformation parameter list is set to be null and recorded by a quantization identifier.
Step 206: obtaining an image compression parameter list according to the macro block transformation result parameter list;
summarizing to obtain an image transformation result parameter list according to the macro block transformation result parameter list; and calculating an image compression parameter list according to the image transformation result parameter list.
Step 207: and performing write compression on the image compression parameter list to obtain a compressed image file.
Specifically, a proper bit width is selected according to an image compression parameter list, and the compression parameters are subjected to write compression; selecting a proper bit width according to the image compression parameter list specifically comprises the following steps:
calculating a corresponding mean value bit width according to the pixel mean value in the image compression parameter list;
calculating corresponding identification bit width according to the quantization identification in the image compression parameter list;
calculating corresponding threshold bit width according to the quantization threshold in the image compression parameter list;
calculating corresponding difference bit width according to the quantized macro block pixel deviation matrix in the image compression parameter list;
the specific calculation method is the same, and the pixel mean is taken as an example to illustrate, the value of the calculated pixel mean N within the range of power N of 2 is the mean bit width, for example, the pixel mean is 12, the value of the calculated pixel mean N within the range of power four of 2 is the mean bit width 4, and so on to obtain the proper bit width;
there are many conventional image compression parameter lists, and it should be noted that the present application is not limited to which method is used, and the following method is used as an example;
the image compression parameter list comprises a pixel mean value reference value, a quantization threshold value reference value, the total number of effective quantization macro blocks, a coding bit width list and a coding information sequence;
the reference value of the pixel mean value is the pixel mean value of the first macro block;
the quantization threshold reference value is the quantization threshold of the first effective quantization macro block;
the total number of the effective quantization macro blocks is the number of the macro blocks with the quantization identification of 1;
the coding bit width list is a bit width list consisting of a mean bit width, an identification bit width, a threshold bit width and a difference bit width;
the coded information sequence comprises a mean sequence, a threshold sequence, an identification sequence and a difference sequence. Specifically, the mean sequence and the identification sequence are obtained by performing differential pulse coding on the pixel mean value and the quantization identification of each macro block according to the corresponding bit width; the threshold value sequence and the difference value sequence are respectively obtained by carrying out differential pulse modulation coding on the threshold value of each effective macro block and the quantized deviation value matrix, wherein the macro block with the differential identifier of 0 only compresses the macro block identifier and the mean residual error.
Coding, writing and compressing the image according to the image compression parameter list to obtain a compressed image file; specifically, the image compression parameter list is encoded and compressed into a compressed image file which can be identified by a computer.
Compared with the image compression method commonly used at present, the method has the advantages that:
1. the calculation amount of the core algorithm steps is greatly reduced, and the algorithm only uses a small amount of addition and subtraction and comparison operations to replace a large amount of addition, subtraction, multiplication and division operations in the existing compression algorithm, so the algorithm can be conveniently realized in a hardware environment;
2. the invention provides adjustable parameters such as a transformation threshold value, and compared with the existing compression algorithm, the invention can more flexibly select the optimal result suitable for the current requirement and adapt to various different compression requirements.
Example two
Referring to fig. 3, the conventional decompression method specifically includes:
step 301: decompressing according to the compressed image file to obtain an image compression parameter list;
step 302: decoding according to the image compression parameter list to obtain a parameter list of each macro block transformation result;
step 303: and performing inverse transformation on each macro block transformation result parameter list and summarizing to obtain a decompressed image.
The second embodiment of the present invention is an improvement on the basis of the existing decompression method, and realizes adjustable lossy compression corresponding to the improved compression method in the first embodiment, and the improved decompression method specifically includes:
a decompression method of hardware-based adjustable lossy compression, as shown in fig. 4, the method comprising:
step 401: decompressing according to the compressed image file to obtain an image compression parameter list;
step 402: decoding the image compression parameter list to obtain a parameter list of each macro block transformation result;
obtaining the pixel mean value of each macro block according to the pixel mean value reference value and the mean value sequence in the coding information sequence;
obtaining a quantization identifier by the total number of the effective quantization macro blocks and an identifier sequence in the coding information sequence;
obtaining the quantization threshold of each effective macro block according to the quantization threshold reference value and the threshold sequence in the coding information sequence;
obtaining a quantized deviation value matrix according to the quantization threshold reference value and the difference value sequence in the coding information sequence;
combining the obtained pixel mean value, the quantization identification, the quantization threshold value and the quantized deviation value matrix of each macro block to obtain a parameter list of the transformation result of each macro block;
step 403: and if the difference of the pixel difference of each macro block transformation result parameter list is larger than a set threshold, executing step 404, otherwise executing step 405:
step 404: restoring each pixel value of the current macro block into the mean value of the pixels of the current macro block and adding a corresponding deviation value, and executing the step 406;
in particular, the method comprises the following steps of,
if the pixel range is greater than the set threshold, the current macroblock is a quantized macroblock, and restoration needs to be performed according to the pixel mean value of the current macroblock and the deviation value matrix in the conversion result parameter list when the quantized macroblock is restored.
Step 405: restoring each pixel value of the current macro block to the pixel average value of the current macro block, and executing step 406;
if the pixel range is less than or equal to the set threshold, it indicates that the current macroblock is not quantized, and when the unquantized macroblock is recovered, the pixel mean value of each macroblock is directly used as the restored pixel value.
Step 406: and summarizing the pixel values of the restored macro blocks to obtain a decompressed image and outputting the decompressed image.
Those of skill in the art will understand that the various exemplary method steps and apparatus elements described in connection with the embodiments disclosed herein can be implemented as electronic hardware, software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative steps and elements have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The various illustrative elements described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.
The steps of a method described in connection with the embodiments disclosed above may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a subscriber station. In the alternative, the processor and the storage medium may reside as discrete components in a subscriber station.
The disclosed embodiments are provided to enable those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the scope or spirit of the invention. The above-described embodiments are merely preferred embodiments of the present invention, which should not be construed as limiting the invention, and any modifications, equivalents, improvements, etc. made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (7)
1. A method for compression based on hardware adjustable lossy compression, comprising:
partitioning an input image, and calculating the pixel mean value and the pixel range of each macro block;
when the pixel range is larger than a set threshold value, quantizing each macro block to obtain a macro block transformation result parameter list;
when the pixel range is not larger than the set threshold, setting a macro block transformation result parameter list according to the pixel mean value; setting specific ranges of a plurality of quantization intervals of the current macro block, and calculating a quantization threshold value and a quantization scale list; obtaining a quantized macro block pixel deviation matrix according to the quantization scale list; setting a macroblock transformation result parameter list according to the quantized macroblock pixel deviation matrix, the pixel mean value of each macroblock, a quantization identifier and a quantization threshold; the obtaining of the quantized macroblock pixel deviation matrix according to the quantization scale list specifically includes: setting endpoints of a plurality of quantization intervals as a preset value group, sequentially determining corresponding values of pixel deviation values in a current macro block in the preset value group as quantization pixel deviations of all positions of the current macro block, and combining the quantization pixel deviations of all the positions of the current macro block to obtain a macro block pixel deviation matrix;
and obtaining an image compression parameter list according to the macro block conversion result parameter list, selecting proper bit width according to the image compression parameter list, and performing write compression on the compression parameters to obtain a compressed image file.
2. The compression method of claim 1, wherein the interactive interface displays a plurality of preset values for the user to select, and the user selects one of the preset values as the set threshold according to the picture quality requirement.
3. The compression method as claimed in claim 1, wherein the calculating of the quantization threshold is to calculate a difference between a maximum value of a pixel of the current macroblock and a mean value of the pixel of the current macroblock, and calculate a difference between the mean value of the pixel of the current macroblock and a minimum value of the pixel of the current macroblock, compare the maximum values of the two difference values, and use the maximum value of the two difference values as the quantization threshold of the current macroblock.
4. The compression method of claim 1, wherein the macroblock transform result parameter list includes a pixel mean, a quantization flag, a quantization threshold of an effective quantized macroblock, and a quantized macroblock pixel deviation matrix.
5. The compression method as claimed in claim 1, wherein said obtaining an image compression parameter list according to the macroblock transform result parameter list comprises:
summarizing to obtain an image transformation result parameter list according to the macro block transformation result parameter list;
and calculating an image compression parameter list according to the image transformation result parameter list.
6. The compression method according to any one of claims 1 to 5, wherein the image compression parameter list includes a pixel mean reference value, a quantization threshold reference value, a total number of valid quantized macroblocks, a coded bit width list, and a coded information sequence.
7. A decompression method for use in the hardware-based scalable lossy compression method according to any one of claims 1 to 6, comprising:
decompressing according to the compressed image file to obtain an image compression parameter list; decoding the image compression parameter list to obtain a transformation result parameter list of each macro block: obtaining the pixel mean value of each macro block according to the pixel mean value reference value and the mean value sequence in the coding information sequence; obtaining the quantization identification of each macro block according to the total number of the effective quantization macro blocks and the identification sequence in the coding information sequence; obtaining the quantization threshold of each effective quantization macro block according to the quantization threshold reference value and the threshold sequence in the coding information sequence; obtaining a quantized deviation value matrix according to the quantization threshold value and the difference value sequence in the coding information sequence; combining the obtained pixel mean value, the quantization identification, the quantization threshold of the effective quantization macro block and the quantized deviation value matrix of each macro block to obtain a transformation result parameter list of each macro block;
when the pixel range in the transformation result parameter list of each macro block is greater than a set threshold value, restoring each pixel value of the current macro block into the pixel mean value of the current macro block plus a corresponding deviation value; when the pixel range in the transformation result parameter list of each macro block is not larger than a set threshold value, restoring each pixel value of the current macro block into the pixel average value of the current macro block;
and summarizing the pixel values of the restored macro blocks to obtain a decompressed image and outputting the decompressed image.
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