WO2005027506A1 - Methods of compressing still pictures for mobile devices - Google Patents

Methods of compressing still pictures for mobile devices Download PDF

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Publication number
WO2005027506A1
WO2005027506A1 PCT/KR2004/002312 KR2004002312W WO2005027506A1 WO 2005027506 A1 WO2005027506 A1 WO 2005027506A1 KR 2004002312 W KR2004002312 W KR 2004002312W WO 2005027506 A1 WO2005027506 A1 WO 2005027506A1
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Prior art keywords
values
sample values
still pictures
compressing
map information
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PCT/KR2004/002312
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French (fr)
Inventor
Changho Lee
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Multivia Co., Ltd.
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
    • H04N1/46—Colour picture communication systems
    • H04N1/64—Systems for the transmission or the storage of the colour picture signal; Details therefor, e.g. coding or decoding means therefor
    • H04N1/644—Systems for the transmission or the storage of the colour picture signal; Details therefor, e.g. coding or decoding means therefor using a reduced set of representative colours, e.g. each representing a particular range in a colour space
    • 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
    • H04N19/90—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using coding techniques not provided for in groups H04N19/10-H04N19/85, e.g. fractals

Definitions

  • the present invention relates to techniques of compressing still pictures. More particularly, the present invention relates to a method of compressing still pictures for mobile devices with high efficiency and no complexity.
  • JPEG Joint Photographic Expert Group
  • DCT Discrete Cosine Transformer
  • JPEG 2000 is a new still picture compressing technology standard based on the wavelet transform, and has better compressibility and functions compared to JPEG.
  • Graphic Interchange Format which is introduced to transmit compressed graphics with high speed through the internet, has a limitation on colors to be used, so that GIF is not cut out for images requiring various colors.
  • Portable Network Graphics PNG is another graphic image compressing technology standard and a lossless compression method. According to PNG, it is made possible to compress graphic image from 10 percent to 30 percent better than GIF, so that it is expected to replace GIF in no time.
  • Figure 1 illustrates a related art method of compressing still pictures according to JPEG.
  • RGB image object of compression
  • SI 2 contrast irrformation and color difference ormation
  • the image is divided into macro block of 16 * 16 pixels and the macro blocks are further divided into blocks of 8 * 8 pixels.
  • the blocks of 8 * 8 pixels are DCT transformed (S 13) and then quantization process for DCT coefficients obtained through DCT transformation is performed (S 14). Finally, the quantized DCT coefficient is entropy coded (S 15).
  • the method of compressing still pictures according to JPEG employs Huffman coding using Huffman table as an entropy coding method.
  • Recently developed CPU of 533MHz has the best performance among CPUs used in mobile device. However, comparing with personal computer, the recently developed CPU used in mobile device still has drawbacks in performance, limited battery and memory.
  • GIF also expresses the graphic images with 256 colors, so that it is ineffective to compress images that can be expressed with less than 256 colors, according to GIF.
  • GIF it is necessary to employ a method of compressing still pictures for mobile devices that effectively compresses still pictures by reconstructing still pictures with appropriate number of colors depending on each still picture.
  • a method of compressing still pictures for mobile devices reconstructs still pictures with different number of colors per each still picture and comprises: checking (R, G, B) values of all pixels in the still pictures; arraying the (R, G, B) values in the order of frequency of existence and making as many (R, G, B) values as it is determined depending on each still picture representative (R, G, B) values; and designating index values for the representative (R, G, B) values and storing the representative (R, G, B) values as palette.
  • the method of compressing still pictures for mobile devices further comprises: obtaining sample values and first map information values by using the index values; and classifying the sample values and compressing the sample values according to the classification.
  • the sample values are generated and have the same value as current index value, in case that previous index value is different from the current index value.
  • the first map information values indicate whether the index values change.
  • the sample values to be compressed are determined according to the first map information values.
  • the act of classifying the sample values and compressing the sample values according to the classification classifies the sample values into low bit sample values and high bit sample values, the low bit sample values being able to be expressed with less than certain number of bits, the hig bit sample values being unable to be expressed with the certain number of bits, and wherein the low bit sample values and the high bit sample values are separately compressed.
  • whether the sample values can be expressed with less than certain number of bits is indicated by a second map information values.
  • Figure 1 illustrates a related art method of compressing still pictures according to JPEG.
  • Figure 2 illustrates a method of compressing still pictures for mobile devices according to the present invention.
  • Figure 3 illustrates a process of reconstructing still pictures with a predetermined number of colors according to a method of compressing still pictures for mobile devices of the present invention.
  • Figure 2 illustrates a method of compressing still pictures for mobile devices according to the present invention.
  • Figure 3 illustrates a process of reconstructing still pictures with a predetermined number of colors according to a method of compressing still pictures of the present invention.
  • still pictures are reconstructed with as many colors as it is determined depending on each still picture, object of compression, and the reconstructed still pictures are compressed.
  • (R, G, B) values of all pixels in the still pictures are checked and arrayed in the order of frequency of existence (e.g. refer to followingtable l) (S31).
  • index value 23 and palette 22 are determined using the 128 (R, G, B) values. For example, index value and palette are determined like following Table 2. [Table 2]
  • the palette 22 consists of representative (R, G, B) values extracted from (R, G, B) values of the still pictures.
  • the index values 23 become index of each (R, G, B) value forming the palette 22.
  • pixels can be expressed with the index values 23 replacing the (R, G, B) values, and palette 22 corresponding to the index values is constructed.
  • the palette 22 is stored as it is to be used as color information of still pictures to be decompressed when decoding is performed.
  • RGB images that are compressed to be used as still pictures for mobile devices are almost simple still pictures and it is general that simple still pictures consists of pixels of same data values continuously, so that it is possible to compress still pictures more effectively.
  • the sample values 25 are designated in this specification first.
  • the sample values 25 are generated when current index value is different from previous index value, and have the same values as the current index values.
  • the first map information values 24 indicate whether the current index value is identical with the previous index value.
  • the first map information values 24 can be expressed by using 1 bit. For example, the first map information value 24 is designated as "1" in case that the current index value is identical with the previous index value, otherwise the first map information value 24 is designated as "0". Conversely, the first map information values 24 can be designated as "0" in case that the current index value is identical with the previous index value, otherwise the first map information value 24 is designated as "1".
  • the sample values 25 are classified into low bit sample values 27 and high bit sample values 26 to be compressed effectively.
  • Sample values that can be expressed by using less than certain number of bits are designated as "low bit sample values” and sample values that requires more than the certain number of bits are designated as "high bit sample values.”
  • the low bit sample values and the high bit sample values are separately arrayed and compressed.
  • second map information values 28 are designated and used to indicate whether the sample values are the low bit sample values 27 or the high bit sample values 26. The second map information values 28 are used for effective performance of compression and decompression. [Table 3]
  • the Table 3 gives examples of first map information values, sample values, low bit sample values, high bit sample values and second map information values depending on change on index values.
  • sample values that can be expressed by using less than 2 bits are designated as low bit sample values and sample values that can not be expressed by using 2 bits are designated as high bit sample values.
  • the second map information value for low bit sample values is designated as "0" and the second map information value for high bit sample values is designated as "1".
  • Specific second map information values for low bit sample values and high bit sample values can be freely designated.
  • the second map information value for low bit sample values can be designated as "1" and the second map information value for high bit sample values can be designated as "0".
  • sample values are classified, depending on whether sample values can be expressed by using less than certain number of bits, into high bit sample values and low bit sample values. Then, according to the classification, high bit sample values and low bit sample values are separately compressed. For example, let us suppose that sample values that can be expressed by using less than 2 bits are low bit sample values and sample values that can not be expressed by using 2 bits are high bit sample values, h this case, 4 low bit sample values can be expressed by using 1 byte. However, just 2 or 3 high bit sample values can be expressed by using 1 byte. Sometimes, 2 bytes are needed to express 3 high bit sample values.

Abstract

The present invention relates to techniques of compressing still pictures. More particularly, the present invention relates to a method of compressing still pictures used in mobile devices with high efficiency and no complexity. According to the present invention, still pictures can be compressed by restructuring the still pictures with different number of colors depending on each still picture. The method of compressing still pictures according to the present invention comprises: obtaining (R, G, B) values of every pixel in the whole still picture; designating (R, G, B) values mainly included in the still picture as representative (R, G, B) values of the still picture, the number of the representative (R, G, B) values being determined depending on each still picture; and giving index values to the representative (R, G, B) values and storing the representative (R, G, B) values in palette.

Description

METHODS OF COMPRESSING STILL PICTURES FOR MOBILE DEVICES
TECHNICAL FIELD The present invention relates to techniques of compressing still pictures. More particularly, the present invention relates to a method of compressing still pictures for mobile devices with high efficiency and no complexity.
BACKGROUND ART Efforts to effectively compress and store image data have been made continuously up to now. As a result, a lot of still picture compressing technologies are used and there are a lot of international standards on still picture compressing technology. Joint Photographic Expert Group (JPEG) is one of widely known technology standard among a lot of still picture compressing technologies. JPEG is based on the mathematical transformation formula called Discrete Cosine Transformer (DCT) and used for still pictures for the internet, digital camera, printer and scanner. On the other hand, JPEG 2000 is a new still picture compressing technology standard based on the wavelet transform, and has better compressibility and functions compared to JPEG. On the other hand, Graphic Interchange Format (GIF), which is introduced to transmit compressed graphics with high speed through the internet, has a limitation on colors to be used, so that GIF is not cut out for images requiring various colors. On the other hand, Portable Network Graphics (PNG) is another graphic image compressing technology standard and a lossless compression method. According to PNG, it is made possible to compress graphic image from 10 percent to 30 percent better than GIF, so that it is expected to replace GIF in no time. Figure 1 illustrates a related art method of compressing still pictures according to JPEG. When RGB image, object of compression, is inputted through means such as camera (Sll), the RGB image is transformed into image in YUV420 format having both contrast irrformation and color difference ormation (SI 2). After that transformation, the image is divided into macro block of 16 * 16 pixels and the macro blocks are further divided into blocks of 8 * 8 pixels. The blocks of 8 * 8 pixels are DCT transformed (S 13) and then quantization process for DCT coefficients obtained through DCT transformation is performed (S 14). Finally, the quantized DCT coefficient is entropy coded (S 15). The method of compressing still pictures according to JPEG employs Huffman coding using Huffman table as an entropy coding method. Up to now, recently developed CPU of 533MHz has the best performance among CPUs used in mobile device. However, comparing with personal computer, the recently developed CPU used in mobile device still has drawbacks in performance, limited battery and memory. Thus, to compress still pictures for mobile device, it is needed to introduce a method of compressing still pictures that can reduce memory usage, display images rapidly without regard to performance of CPU, and have effective compressibility. In general, images are expressed with 24 bits colors (R(red)- 8bits, G(green)- 8bits, B(blue)-8bits) and simple images such as graphic, animation and menu screen are also expressed with 24 bit colors. However, there are a lot of simple still pictures that can be expressed with less than 256 colors. Thus, it is ineffective to express such simple still pictures with 256 colors mctiscriminately. On the other hand, there is a codec such as GIF developed for graphic images more simple than real images. However, GIF also expresses the graphic images with 256 colors, so that it is ineffective to compress images that can be expressed with less than 256 colors, according to GIF. Thus, it is necessary to employ a method of compressing still pictures for mobile devices that effectively compresses still pictures by reconstructing still pictures with appropriate number of colors depending on each still picture.
DISCLOSURE OF INVENTION In order to solve problems and subjects of the related art, there is provided a method of effectively compressing simple images (e.g. graphic, animation, etc.) for mobile communication image service. A method of compressing still pictures for mobile devices reconstructs still pictures with different number of colors per each still picture and comprises: checking (R, G, B) values of all pixels in the still pictures; arraying the (R, G, B) values in the order of frequency of existence and making as many (R, G, B) values as it is determined depending on each still picture representative (R, G, B) values; and designating index values for the representative (R, G, B) values and storing the representative (R, G, B) values as palette. Preferably, the method of compressing still pictures for mobile devices according to the present invention further comprises: obtaining sample values and first map information values by using the index values; and classifying the sample values and compressing the sample values according to the classification. Preferably, the sample values are generated and have the same value as current index value, in case that previous index value is different from the current index value. Preferably, the first map information values indicate whether the index values change. Preferably, the sample values to be compressed are determined according to the first map information values. Preferably, the act of classifying the sample values and compressing the sample values according to the classification classifies the sample values into low bit sample values and high bit sample values, the low bit sample values being able to be expressed with less than certain number of bits, the hig bit sample values being unable to be expressed with the certain number of bits, and wherein the low bit sample values and the high bit sample values are separately compressed. Preferably, whether the sample values can be expressed with less than certain number of bits is indicated by a second map information values.
BRIEF DESCRJTPTION OF THE DRAWINGS Figure 1 illustrates a related art method of compressing still pictures according to JPEG. Figure 2 illustrates a method of compressing still pictures for mobile devices according to the present invention. Figure 3 illustrates a process of reconstructing still pictures with a predetermined number of colors according to a method of compressing still pictures for mobile devices of the present invention.
BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, a detailed description on a method of compressing still pictures for mobile devices will be made with reference to the attached drawings. Figure 2 illustrates a method of compressing still pictures for mobile devices according to the present invention. Figure 3 illustrates a process of reconstructing still pictures with a predetermined number of colors according to a method of compressing still pictures of the present invention. According to a method of compressing still pictures for mobile devices of the present invention, still pictures are reconstructed with as many colors as it is determined depending on each still picture, object of compression, and the reconstructed still pictures are compressed. For reconstruction of the still pictures, (R, G, B) values of all pixels in the still pictures are checked and arrayed in the order of frequency of existence (e.g. refer to followingtable l) (S31). [Table 1]
Figure imgf000007_0001
Then, as many (R, G, B) values as it is predetermined for reconstruction of the still pictures are arrayed. For example, to reconstruct still pictures having (R, G, B) values arrangement like the Table 1 with 128 colors, 128 (R, G, B) values from the most frequent
(R, G, B) value (100, 110, 120) are arranged. Index value 23 and palette 22 are determined using the 128 (R, G, B) values. For example, index value and palette are determined like following Table 2. [Table 2]
Figure imgf000008_0001
The palette 22 consists of representative (R, G, B) values extracted from (R, G, B) values of the still pictures. The index values 23 become index of each (R, G, B) value forming the palette 22. Through the above processes, pixels can be expressed with the index values 23 replacing the (R, G, B) values, and palette 22 corresponding to the index values is constructed. The palette 22 is stored as it is to be used as color information of still pictures to be decompressed when decoding is performed. On the other hand, RGB images that are compressed to be used as still pictures for mobile devices are almost simple still pictures and it is general that simple still pictures consists of pixels of same data values continuously, so that it is possible to compress still pictures more effectively. This can be utilized in a process of obtaining sample values 25 and first map information values 24 from the index values 23. The sample values 25 are designated in this specification first. The sample values 25 are generated when current index value is different from previous index value, and have the same values as the current index values. The first map information values 24 indicate whether the current index value is identical with the previous index value. The first map information values 24 can be expressed by using 1 bit. For example, the first map information value 24 is designated as "1" in case that the current index value is identical with the previous index value, otherwise the first map information value 24 is designated as "0". Conversely, the first map information values 24 can be designated as "0" in case that the current index value is identical with the previous index value, otherwise the first map information value 24 is designated as "1". After obtaining the sample values 25 and the first map information values 24 from the index values 23, the sample values 25 are classified into low bit sample values 27 and high bit sample values 26 to be compressed effectively. Sample values that can be expressed by using less than certain number of bits are designated as "low bit sample values" and sample values that requires more than the certain number of bits are designated as "high bit sample values." At this time, the low bit sample values and the high bit sample values are separately arrayed and compressed. Further, second map information values 28 are designated and used to indicate whether the sample values are the low bit sample values 27 or the high bit sample values 26. The second map information values 28 are used for effective performance of compression and decompression. [Table 3]
Figure imgf000009_0001
Figure imgf000010_0001
The Table 3 gives examples of first map information values, sample values, low bit sample values, high bit sample values and second map information values depending on change on index values. In the Table 3, sample values that can be expressed by using less than 2 bits are designated as low bit sample values and sample values that can not be expressed by using 2 bits are designated as high bit sample values. The second map information value for low bit sample values is designated as "0" and the second map information value for high bit sample values is designated as "1". Specific second map information values for low bit sample values and high bit sample values can be freely designated. Thus, conversely, the second map information value for low bit sample values can be designated as "1" and the second map information value for high bit sample values can be designated as "0". As described above, sample values are classified, depending on whether sample values can be expressed by using less than certain number of bits, into high bit sample values and low bit sample values. Then, according to the classification, high bit sample values and low bit sample values are separately compressed. For example, let us suppose that sample values that can be expressed by using less than 2 bits are low bit sample values and sample values that can not be expressed by using 2 bits are high bit sample values, h this case, 4 low bit sample values can be expressed by using 1 byte. However, just 2 or 3 high bit sample values can be expressed by using 1 byte. Sometimes, 2 bytes are needed to express 3 high bit sample values.
INDUSTRIAL APPLICABILITY According to a method of compressing still pictures for mobile devices, still pictures are compressed by being reconstructed not with number of colors predetermined without regard to the still pictures but with the number of colors appropriate for each still picture, so that it is made possible to compress still pictures with high compressibility in more simple way than a related art, to display still pictures rapidly and to m mize usage of memory and battery. Although the preferred embodiments of the present invention have been disclosed for illustrative purposes, those skilled in the art will appreciate that various modifications, additions and substitutions are possible, without departing from the scope and spirit of the invention as disclosed in the accompanying claims.

Claims

WHAT IS CLAIMED IS: 1. A method of compressing still pictures for mobile devices that reconstructs still pictures with different number of colors per each still picture.
2. The method of claim 1 , comprising: checking (R, G, B) values of all pixels in the still pictures; arraying the (R, G, B) values in the order of frequency of existence and making as many (R, G, B) values as it is determined depending on each still picture representative (R, G, B) values; and designating index values for the representative (R, G, B) values and storing the representative (R, G, B) values as palette.
3. The method of claim 2, further comprising: obtaining sample values and first map information values by using the index values; and classifying the sample values and compressing the sample values according to the classification.
4. The method of claim 3, wherein the sample values are generated and have the same value as current index value, in case that previous index value is different from the current index value.
5. The method of claim 3, wherein the first map information values indicate whether the index values change.
6. The method of claim 3, wherein sample values to be compressed are determined according to the first map information values.
7. The method of claim 3, wherein the act of classifying the sample values and compressing the sample values according to the classification classifies the sample values into low bit sample values and high bit sample values, the low bit sample values being able to be expressed with less than certain number of bits, the high bit sample values being unable to be expressed with the certain number of bits, and wherein the low bit sample values and the high bit sample values are separately compressed.
8. The method of claim 7, wherein whether the sample values can be expressed with less than certain number of bits is indicated by second map information values.
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