WO2018103568A1 - 云桌面内容编码与解码方法及装置、系统 - Google Patents

云桌面内容编码与解码方法及装置、系统 Download PDF

Info

Publication number
WO2018103568A1
WO2018103568A1 PCT/CN2017/113626 CN2017113626W WO2018103568A1 WO 2018103568 A1 WO2018103568 A1 WO 2018103568A1 CN 2017113626 W CN2017113626 W CN 2017113626W WO 2018103568 A1 WO2018103568 A1 WO 2018103568A1
Authority
WO
WIPO (PCT)
Prior art keywords
image data
macroblock
type
cloud desktop
class
Prior art date
Application number
PCT/CN2017/113626
Other languages
English (en)
French (fr)
Inventor
朱海涛
吴迪
崔振峰
Original Assignee
中兴通讯股份有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 中兴通讯股份有限公司 filed Critical 中兴通讯股份有限公司
Publication of WO2018103568A1 publication Critical patent/WO2018103568A1/zh

Links

Images

Classifications

    • 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
    • H04N19/169—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
    • H04N19/17—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 an image region, e.g. an object
    • H04N19/176—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 an image region, e.g. an object the region being a block, e.g. a macroblock
    • 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
    • H04N19/103—Selection of coding mode or of prediction mode
    • H04N19/107—Selection of coding mode or of prediction mode between spatial and temporal predictive coding, e.g. picture refresh
    • 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
    • H04N19/20—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using video object coding
    • H04N19/21—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using video object coding with binary alpha-plane coding for video objects, e.g. context-based arithmetic encoding [CAE]
    • 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
    • H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
    • H04N19/503—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving temporal prediction
    • H04N19/51—Motion estimation or motion compensation
    • 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
    • H04N19/91—Entropy coding, e.g. variable length coding [VLC] or arithmetic coding

Definitions

  • the present disclosure relates to the field of image compression coding and decoding technologies, and in particular, to a cloud desktop content encoding and decoding method, apparatus, and system.
  • a desktop image containing cloud desktop content is a blended image that contains a subset of non-continuous tonal variations, such as text, icons, graphics, and a subset of continuous tonal variations, such as natural images, video, and more.
  • the standard H.264 encoder encodes a sequence of images with continuous tone changes, which has a good coding effect, but when encoding text or graphics regions, the decoded text or graphics will be introduced due to the loss of coding. It shows edge burrs, distortion and so on.
  • the H.264 encoder can improve the compression efficiency by utilizing the correlation of the spatial domain time domain. However, if the desktop image is directly encoded, the decoded text or graphic display may be blurred or distorted, which may affect the user experience.
  • the present disclosure proposes a cloud desktop content encoding and decoding method, apparatus, and system.
  • the present disclosure provides a cloud desktop content encoding method, the method comprising:
  • the macroblock type is the first type
  • the macroblock is subjected to lossless compression coding
  • the macroblock type is the second type
  • the macroblock is subjected to lossy compression coding, wherein ,
  • the first type includes a text class or a graphic class
  • the second type includes an image class
  • the step of performing lossless compression encoding on the macroblock includes:
  • the prediction residual is transformed according to a preset compression method, and the transformed prediction residual is reordered, and context-based adaptive binary arithmetic CABAC entropy coding is performed.
  • the method further includes:
  • the obtained image data is retained.
  • the method further includes:
  • the PCM-type macroblock is compression-encoded according to the Zip format to obtain corresponding image data.
  • the step of performing lossy compression coding on the macroblock includes:
  • the method before the encoded image data and the macroblock type corresponding to the image data are sent to the client, the method further includes:
  • the present disclosure also provides a cloud desktop content decoding method, where the method includes:
  • the image data is Performing decoding according to a decoding manner corresponding to the lossless compression coding. If the macroblock type corresponding to the image data is the second type, decoding the image data according to a decoding manner corresponding to the lossy compression coding, where the The first type includes a text class or a graphics class, and the second type includes an image class.
  • the receiving, by the plurality of macroblocks, the encoded image data divided by the desktop image of the cloud desktop, and the macroblock type corresponding to the image data further includes:
  • the image data is decoded in accordance with the Zip format.
  • the cloud desktop content decoding method further includes:
  • the received image data After decoding the received image data, it is detected whether the desktop image formed by the macroblock generated after decoding is a reference image, and if so, the generated macroblock is subjected to deblocking filtering.
  • the present disclosure further provides a cloud desktop content encoding apparatus, where the apparatus includes:
  • a classification module configured to divide a desktop image of the cloud desktop into a plurality of macroblocks according to a preset manner, and determine a macroblock type corresponding to each of the plurality of macroblocks;
  • An encoding module configured to perform lossless compression encoding on the macroblock when the macroblock type is the first type, and to perform the macroblock in the case that the macroblock type is the second type Loss compression coding, wherein the first type comprises a text class or a graphics class, and the second type comprises an image class;
  • the transmission module is configured to send the encoded image data and the macroblock type corresponding to the image data to the client.
  • the encoding module includes:
  • a lossless compression coding unit configured to perform intra prediction and inter prediction on the macroblock to obtain a prediction residual; and transform the prediction residual according to a preset compression method, and re-transform the transformed prediction residual Context-based adaptive binary arithmetic CABAC entropy coding is performed after sorting.
  • the lossless compression coding unit is further configured to:
  • the obtained image data is retained.
  • the encoding module further includes:
  • the Zip compression coding unit is configured to compress and encode the macroblock of the PCM class according to a Zip format to obtain corresponding image data.
  • the encoding module further includes:
  • a lossy compression coding unit configured to perform intra prediction and inter prediction on the macroblock to obtain a prediction residual; and perform integer DCT transform on the prediction residual to obtain frequency domain residual data, and the frequency is
  • the CABAC entropy coding is performed after the domain residual data is quantized.
  • the cloud desktop content encoding apparatus further includes:
  • the first deblocking filtering module is configured to detect whether the desktop image is a reference image before sending the encoded image data and the macroblock type corresponding to the image data to the client, and if so, the lossy compression coding The resulting image data is subjected to deblocking filtering.
  • the present disclosure also provides a cloud desktop content decoding apparatus, where the apparatus includes:
  • a receiving module configured to receive, by the plurality of macroblocks, the encoded image data divided by the desktop image of the cloud desktop, and a macroblock type corresponding to the image data;
  • a decoding module configured to decode the image data according to a decoding manner corresponding to lossless compression encoding, where a macroblock type corresponding to the image data is a first type, and a macroblock type corresponding to the image data is In the case of the second type, the image data is decoded according to a decoding mode corresponding to the lossy compression coding, wherein the first type includes a character class or a graphics class, and the second type includes an image class.
  • the decoding module is further configured to:
  • the image data is decoded in accordance with the Zip format.
  • the cloud desktop content decoding apparatus further includes:
  • a second deblocking filtering module configured to: after decoding the received image data, detecting whether the desktop image formed by the decoded macroblock is a reference image, and if so, generating the The macroblock performs deblocking filtering.
  • the present disclosure further provides a cloud desktop content encoding and decoding system, the system comprising a cloud desktop content encoding device and a cloud desktop content encoding device, wherein the cloud desktop content encoding device is the cloud desktop content encoding device,
  • the cloud desktop content decoding device is the cloud desktop content decoding device described above.
  • Embodiments of the present disclosure also provide a computer readable storage medium storing computer executable instructions that, when executed by a processor, implement any of the methods described above.
  • the cloud desktop content encoding and decoding method, device and system divide the desktop image of the cloud desktop into a plurality of macroblocks, and then perform lossless compression encoding on the macroblocks of the text class or the graphics class, and the image class
  • the macroblock performs lossy compression coding, and sends the encoded image data and the macroblock type corresponding to the image data to the client; in addition, the client receives the image data and the macroblock corresponding to the image data.
  • the image data of the macroblock compression coding of the character class or the graphics class is decoded according to the decoding method corresponding to the lossless compression coding, and the image data corresponding to the macroblock compression coding of the image class is decoded according to the lossy compression coding.
  • the macro file of the character class or the graphic class adopts a decoding method corresponding to lossless compression coding and lossless compression coding, so the client will use the character class.
  • the original data can be completely restored without causing any distortion, and the problem that the desktop image of the cloud desktop is blurred after the desktop image is sent to the client is solved, thereby improving the user experience. .
  • FIG. 1 is a schematic flow chart of a first example of a cloud desktop content encoding method of the present disclosure
  • FIG. 2 is a schematic diagram showing the refinement steps of step S120 shown in FIG. 1 of the cloud desktop content encoding method of the present disclosure
  • step S120 shown in FIG. 1 of the cloud desktop content encoding method of the present disclosure
  • FIG. 4 is a schematic flowchart diagram of a second example of the cloud desktop content encoding method of the present disclosure
  • FIG. 5 is a schematic flowchart diagram of a first example of a cloud desktop content decoding method according to the present disclosure
  • FIG. 6 is a schematic flowchart diagram of a second example of the cloud desktop content decoding method of the present disclosure.
  • FIG. 7 is a block diagram showing a first example of a cloud desktop content encoding apparatus of the present disclosure
  • FIG. 8 is a schematic diagram of a refinement module of the encoding module 120 shown in FIG. 7 of the cloud desktop content encoding apparatus of the present disclosure
  • FIG. 9 is a schematic diagram of another refinement module of the encoding module 120 shown in FIG. 7 of the cloud desktop content encoding apparatus of the present disclosure.
  • FIG. 10 is a block diagram showing a second example of the cloud desktop content encoding apparatus of the present disclosure.
  • FIG. 11 is a block diagram showing a first example of a cloud desktop content decoding apparatus of the present disclosure
  • FIG. 12 is a block diagram showing a second example of the cloud desktop content decoding apparatus of the present disclosure.
  • FIG. 13 is a schematic structural diagram of a cloud desktop content encoding and decoding system of the present disclosure.
  • FIG. 1 is a schematic flowchart diagram of a first example of a cloud desktop content encoding method according to the present disclosure.
  • the cloud desktop content encoding method includes:
  • Step S110 dividing the desktop image of the cloud desktop into a plurality of macroblocks according to a preset manner, and determining macroblock types corresponding to the plurality of macroblocks respectively.
  • the content of the cloud desktop refers to content requested by the user at the cloud server, such as text, graphics, images, or video
  • the content of the cloud desktop is generally composed of a plurality of desktop images.
  • the cloud server compresses and encodes the content of the cloud desktop before sending the content of the cloud desktop to the client.
  • each desktop image is first divided into a plurality of macroblocks, each macroblock includes 16 ⁇ 16 pixels, and then each macroblock is determined to correspond to each
  • the macroblock type, the macroblock can be mainly divided into a text class, a graphic class, and an image class.
  • Step S120 if the macroblock type is the first type, perform the macro block without Loss compression coding, in the case where the macroblock type is the second type, performing lossy compression coding on the macroblock, wherein the first type includes a text class or a graphics class, and the second type includes an image class.
  • a macroblock of a character class or a graphics class is subjected to lossless compression coding
  • a macroblock of an image class is subjected to lossy compression coding.
  • lossless compression coding is to use the redundancy of data for compression, and can completely recover the original data without causing any distortion, and is more suitable for macro blocks of text class or graphic class.
  • Lossy compression coding utilizes the characteristics that humans are insensitive to certain frequency components in the image, allowing a certain amount of information to be lost during compression. Although the original data cannot be completely recovered, the loss of the part has less influence on understanding the original image. And the compression is relatively high, which is more suitable for the macroblock of the image class.
  • the lossy compression coding and the lossless compression coding may be based on the H.264 compression coding standard.
  • Step S130 Send the encoded image data and the macroblock type corresponding to the image data to the client.
  • the macroblock types corresponding to the image data and the image data respectively compressed and encoded by the plurality of macroblocks are simultaneously transmitted to the client.
  • the cloud desktop content encoding method described in this example divides the desktop image of the cloud desktop into a plurality of macroblocks, and then performs lossless compression encoding on the macroblocks of the text class or the graphics class to perform lossy compression on the macroblocks of the image class. Encoding, and transmitting the encoded image data and the macroblock type corresponding to the image data to the client, because the macroblock of the text class or the graphic class is used for lossless compression when encoding the desktop image of the cloud desktop Encoding, so the client decodes the image data corresponding to the macro block of the text class or the graphic class, and can completely recover the original data without causing any distortion, and solves the text or graphic after the desktop image of the cloud desktop is sent to the client. Displaying ambiguous issues increases the user experience.
  • FIG. 2 is a schematic diagram showing the refinement steps of step S120 shown in FIG. 1 of the cloud desktop content encoding method of the present disclosure.
  • the step of performing lossless compression coding on the macroblock in the foregoing step S120 includes:
  • Step S121 performing intra prediction and inter prediction on the macroblock to obtain a prediction residual
  • Step S122 transforming the prediction residual according to a preset compression method, and transforming
  • the context-based adaptive binary arithmetic CABAC entropy coding is performed after the subsequent prediction residuals are reordered.
  • the macroblock type is the first type
  • performing intra prediction and inter prediction on the first type of macroblock to obtain a prediction residual and then performing the prediction by using a Transform_bypass type transformation manner.
  • the residual is transformed, and the prediction parameters are not subjected to frequency domain transformation and quantization, thereby avoiding coding loss.
  • context-based adaptive binary arithmetic coding that is, CABAC (Context-based Adaptive Binary Arithmetic Coding) entropy coding, is performed to obtain image data of the first type of macroblock compression coding.
  • CABAC Context-based Adaptive Binary Arithmetic Coding
  • the method further includes:
  • the number of bits of the encoded image data is greater than a preset threshold. If it is greater, the obtained image data is discarded, and the encoding type of the macroblock is set.
  • the PCM class it is equivalent to adding an encoding label to the image data whose number of bits is greater than a preset threshold; if the number of bits of the encoded image data is less than or equal to a preset threshold, the obtained image data is retained.
  • an array mb_is_pcm having the same size as the plurality of macroblocks is created. All elements in the array mb_is_pcm are initialized to 0 before the compression of the plurality of macroblocks.
  • the macroblock type is the first type, if the number of bits of the obtained image data after the lossless compression encoding of the macroblock is greater than a preset threshold, the obtained image data is discarded, and the obtained image data is The encoding type of the macroblock is set to the PCM class, and the array mb_is_pcm[mb_xy] is set to 1, where "mb_xy" is the index information of the macroblock of the encoding type PCM. If the number of bits of the encoded image data is less than or equal to a preset threshold, the obtained image data is retained, and the array mb_is_pcm remains unchanged.
  • FIG. 3 is a schematic diagram of another refinement step of step S120 shown in FIG. 1 of the cloud desktop content encoding method of the present disclosure.
  • the step of performing lossy compression coding on the macroblock includes:
  • Step S123 performing intra prediction and inter prediction on the macroblock to obtain a prediction residual
  • Step S124 Perform integer DCT transform on the prediction residual to obtain frequency domain residual data, and quantize the frequency domain residual data to perform the CABAC entropy coding.
  • the prediction residual is subjected to integer DCT transform to obtain frequency domain residual data.
  • CABAC entropy coding CABAC entropy coding
  • the cloud desktop content encoding method described in this example performs lossy compression coding on a macroblock of an image class by performing lossless compression encoding on a macroblock of a character class or a graphics class, and performs macroblock on a character class or a graphics class.
  • lossless compression coding if the number of bits of the encoded image data is greater than a preset threshold, the obtained image data is discarded, and the coding type of the macroblock of the character or graphics class is set to the PCM class, which enables A macroblock of a text class or a graphics class does not cause data loss after compression encoding, and can increase the rate of compression encoding.
  • the method further includes:
  • the PCM-type macroblock is compression-encoded according to the Zip format to obtain corresponding image data.
  • the PCM-type macroblock After performing compression coding on all macroblocks, it is detected whether there is a macroblock of the PCM type, and if so, the PCM-type macroblock is compression-encoded according to the Zip format to obtain the PCM class. The image data corresponding to the macro block.
  • the array mb_is_pcm contains two elements with values of 1, mb_is_pcm[mb_x1y1], mb_is_pcm[mb_x2y2], and mb_is_pcm[mb_x1y1], mb_is_pcm[mb_x2y2]
  • Corresponding macroblocks are compression-encoded according to the Zip format to obtain the PCM Image data corresponding to a macroblock of a class. And the obtained image data corresponding to the macroblock of the PCM class is encapsulated into a NAL packet conforming to the H.264 compression coding standard.
  • the macroblock of the PCM class may be compression-encoded according to the Zip format to obtain corresponding image data.
  • the cloud desktop content encoding method described in this example detects whether there is a macroblock of the PCM type after the compression encoding of all the macroblocks, and if so, the macroblock of the PCM class is performed according to the Zip format. Compression coding obtains image data corresponding to macroblocks of the PCM class.
  • the macroblock of the character class or the graphics class is compression-encoded, if the number of bits of the encoded image data is greater than a preset threshold, the obtained image data is discarded, and the encoding method of the Zip format is adopted.
  • the macroblock can be compression-encoded at a higher compression rate and compression speed, which can effectively improve the coding efficiency of the cloud desktop content.
  • FIG. 4 is a schematic flowchart diagram of a second example of the cloud desktop content encoding method of the present disclosure. Based on the example described in FIG. 1 above, in this example, the cloud desktop content encoding method of the present disclosure further includes:
  • Step S140 detecting whether the desktop image is a reference image, and if so, performing deblocking filtering on the image data obtained by lossy compression coding.
  • the encoded image data and the macroblock type corresponding to the image data are sent to the client, whether the desktop image is a reference image is detected, and if so, the image data obtained by lossy compression encoding is obtained. Perform deblocking filtering.
  • the standard H.264 encoding method performs deblocking filtering on all macroblock compression-coded image data, but compresses and encodes the macroblocks of the text or graphics type in the non-continuous tone region.
  • the deblocking filtering of the data will cause significant distortion, so in this example, the image data of the macroblock compression coding of the character class or the graphics class is not subjected to deblocking filtering.
  • the current macroblock and the character class are not Deblocking filtering is performed on the adjacent vertical boundaries of the macroblocks of the graphics class.
  • the obtained image data is subjected to the deblocking filtering process in the horizontal direction, if the macroblock adjacent to the upper side of the current macroblock belongs to a macroblock of a character class or a graphics class, the current macroblock and the character class or the graphics class are not Deblocking filtering is performed on the adjacent horizontal boundaries of the macroblocks.
  • the cloud desktop content encoding method before the encoded image data and the macroblock type corresponding to the image data are sent to the client, detecting whether the desktop image is a reference image, and if so, the lossy compression coding station
  • the obtained image data is subjected to deblocking filtering, which effectively avoids the problem that the obtained desktop image has a square effect after the image data is decoded.
  • FIG. 5 is a schematic flowchart diagram of a first example of a cloud desktop content decoding method according to the present disclosure.
  • the cloud desktop content decoding method includes:
  • Step S210 receiving image data of a plurality of macroblocks compression-encoded by the desktop image of the cloud desktop, and a macroblock type corresponding to the image data.
  • the client after the client sends the acquisition request to the cloud server, the client receives the image data compressed and encoded by the plurality of macroblocks and the macroblock type corresponding to the image data.
  • Step S220 If the macroblock type corresponding to the image data is the first type, the image data is decoded according to a decoding manner corresponding to the lossless compression encoding, and the macroblock type corresponding to the image data is the second type.
  • the image data is decoded according to a decoding mode corresponding to the lossy compression coding, wherein the first type includes a character class or a graphics class, and the second type includes an image class.
  • the image data is separately decoded according to the macroblock type corresponding to the image data.
  • the macroblock type corresponding to the image data is a character class or a graphic class
  • the image data is not damaged.
  • the decoding method corresponding to the compression coding is decoded, and when the macroblock type corresponding to the image data is an image class, the image data is decoded according to a decoding method corresponding to the lossy compression coding.
  • the desktop image formed by the macroblock generated by decoding the image data is displayed on the client.
  • the client after receiving the image data of the macroblock compression-encoded by the desktop image of the cloud desktop, and the macroblock type corresponding to the image data, the client further includes:
  • the image data is decoded in accordance with the Zip format.
  • the client receives the compression code of multiple macroblocks divided by the desktop image of the cloud desktop. And the image data, and the macroblock type corresponding to the image data, determining whether there is image data compression-encoded by the macroblock of the PCM type in the image data, and if present, the macroblock of the PCM type
  • the compression-encoded image data is decoded in accordance with the Zip format.
  • the image data when the image data is decoded, if image data that is compression-encoded by the first type of macroblock is encountered, it is determined whether the coding type of the macroblock is a PCM class; if it is a non-PCM class, Decoding the image data according to a decoding method corresponding to the lossless compression encoding; if it is determined that the encoding type of the macroblock is a PCM class, the image data is not decoded according to a decoding method corresponding to the lossless compression encoding, and jumping to the next A macroblock compresses the encoded image data for decoding.
  • the coding type of the macroblock if image data that is compression-encoded by the first type of macroblock is encountered, it is determined whether the coding type of the macroblock is a PCM class; if it is a non-PCM class, Decoding the image data according to a decoding method corresponding to the lossless compression encoding; if it is determined that the encoding type of the macro
  • the image data when the image data is decoded, it may also be determined whether the coding type of the macroblock is a PCM class when the image data is encoded by the first type of macroblock compression; if it is a non-PCM class And decoding the image data according to a decoding method corresponding to lossless compression coding; if it is determined that the coding type of the macro block is a PCM class, the image data is decoded according to a Zip format.
  • the character class is The image data of the macroblock compression coding of the graphics type is decoded according to the decoding method corresponding to the lossless compression coding, and the image data of the macroblock compression coding of the image type is decoded according to the decoding method corresponding to the lossy compression coding.
  • the macro block of the text class or the graphic class adopts a decoding mode corresponding to the lossless compression coding, so the client will use the macro block of the text class or the graphic class.
  • the original data can be completely restored without causing any distortion, which solves the problem that the desktop image of the cloud desktop is blurred after the text is sent to the client, thereby improving the user experience.
  • FIG. 6 is a schematic flowchart diagram of a second example of the cloud desktop content decoding method of the present disclosure. Based on the example described in FIG. 5 above, in this example, the cloud desktop content decoding method further includes:
  • Step S230 after decoding the received image data, detecting whether the desktop image formed by the macroblock generated after decoding is a reference image, and if so, deblocking the generated macroblock. Effect filtering.
  • the macroblock generated after decoding After decoding the received image data, if it is detected that the desktop image formed by the macroblock generated after decoding is a reference image, only the macroblock generated after decoding the image data corresponding to the macroblock of the image class Perform deblocking filtering. Wherein, when the macroblock is subjected to the deblocking filtering process in the vertical direction, if the left macroblock of the current macroblock belongs to a macroblock of a character class or a graphics class, the current macroblock and the character class are not Deblocking filtering is performed on the adjacent vertical boundaries of the macroblocks of the graphics class.
  • the macroblock generated by decoding the image data corresponding to the macroblock of the image class is subjected to the deblocking filtering process in the horizontal direction, if the macroblock adjacent to the upper side of the current macroblock belongs to a macroblock of a character class or a graphics class, The current macroblock performs deblocking filtering on a horizontal boundary adjacent to the macroblock of the character class or the graphics class.
  • the cloud desktop content decoding method after decoding the received image data compression-encoded by the plurality of macroblocks, if the desktop image formed by the macroblock generated after the decoding of the image data is detected is
  • the reference image performs deblocking filtering processing on the macroblock generated after decoding the image data corresponding to the macroblock of the image class, thereby effectively avoiding the problem of square effect of the obtained desktop image after decoding the image data.
  • FIG. 7 is a block diagram of a first example of a cloud desktop content encoding apparatus of the present disclosure.
  • the cloud desktop content encoding apparatus 100 includes:
  • the classification module 110 is configured to divide the desktop image of the cloud desktop into a plurality of macroblocks according to a preset manner, and determine a macroblock type corresponding to each of the plurality of macroblocks.
  • the content of the cloud desktop refers to content requested by the user at the cloud server, such as text, graphics, images, or video
  • the content of the cloud desktop is generally composed of a plurality of desktop images.
  • the cloud server compresses and encodes the content of the cloud desktop before sending the content of the cloud desktop to the client.
  • each desktop image is first divided into a plurality of macroblocks, each macroblock includes 16 ⁇ 16 pixels, and then each macroblock is determined to correspond to each
  • the macroblock type, the macroblock can be mainly divided into a text class, a graphic class, and an image class.
  • the encoding module 120 is configured to perform lossless compression encoding on the macroblock when the macroblock type is the first type, and perform the macroblock in the case that the macroblock type is the second type Lossy compression coding, wherein the first type comprises a literal class or a graphics class, the second type Includes image classes.
  • a macroblock of a character class or a graphics class is subjected to lossless compression coding
  • a macroblock of an image class is subjected to lossy compression coding.
  • lossless compression coding is to use the redundancy of data for compression, and can completely recover the original data without causing any distortion, and is more suitable for macro blocks of text class or graphic class.
  • Lossy compression coding utilizes the characteristics that humans are insensitive to certain frequency components in the image, allowing a certain amount of information to be lost during compression. Although the original data cannot be completely recovered, the loss of the part has less influence on understanding the original image. And the compression is relatively high, which is more suitable for the macroblock of the image class.
  • the lossy compression coding and the lossless compression coding may be based on the H.264 compression coding standard.
  • the transmission module 130 is configured to send the encoded image data and the macroblock type corresponding to the image data to the client.
  • the macroblock types corresponding to the plurality of macroblock compression-encoded image data and the image data are respectively sent to the client.
  • the cloud desktop content encoding apparatus of the present example performs lossy compression on a macroblock of an image class by dividing a desktop image of the cloud desktop into a plurality of macroblocks, and then performing lossless compression encoding on a macroblock of a character class or a graphics class. Encoding, and transmitting the encoded image data and the macroblock type corresponding to the image data to the client, because the macroblock of the text class or the graphic class is used for lossless compression when encoding the desktop image of the cloud desktop Encoding, so the client decodes the image data corresponding to the macro block of the text class or the graphic class, and can completely recover the original data without causing any distortion, and solves the text or graphic after the desktop image of the cloud desktop is sent to the client. Displaying ambiguous issues increases the user experience.
  • FIG. 8 is a schematic diagram of a refinement module of the encoding module 120 shown in FIG. 7 of the cloud desktop content encoding apparatus of the present disclosure. Based on the example described in FIG. 7 above, in the present example, the foregoing encoding module 120 includes:
  • the lossless compression coding unit 121 is configured to perform intra prediction and inter prediction on the macroblock to obtain a prediction residual; and transform the prediction residual according to a preset compression method, and convert the transformed prediction residual After reordering, context-based adaptive binary arithmetic CABAC entropy coding is performed.
  • the macroblock type is the first type
  • performing intra prediction and inter prediction on the first type of macroblock to obtain a prediction residual and then performing the prediction by using a Transform_bypass type transformation manner.
  • the residual is transformed, and the prediction parameters are not subjected to frequency domain transformation and quantization, thereby avoiding coding loss.
  • context-based adaptive binary arithmetic coding that is, CABAC (Context-based Adaptive Binary Arithmetic Coding) entropy coding, is performed to obtain image data of the first type of macroblock compression coding.
  • CABAC Context-based Adaptive Binary Arithmetic Coding
  • the lossless compression encoding unit 121 is further configured to:
  • the number of bits of the encoded image data is greater than a preset threshold. If it is greater, the obtained image data is discarded, and the encoding type of the macroblock is set.
  • the PCM class it is equivalent to adding an encoding label to the image data whose number of bits is greater than a preset threshold; if the number of bits of the encoded image data is less than or equal to a preset threshold, the obtained image data is retained.
  • an array mb_is_pcm having the same size as the plurality of macroblocks is created. All elements in the array mb_is_pcm are initialized to 0 before the compression of the plurality of macroblocks.
  • the macroblock type is the first type, if the number of bits of the obtained image data after the lossless compression encoding of the macroblock is greater than a preset threshold, the obtained image data is discarded, and the obtained image data is The encoding type of the macroblock is set to the PCM class, and the array mb_is_pcm[mb_xy] is set to 1, where "mb_xy" is the index information of the macroblock of the encoding type PCM. If the number of bits of the encoded image data is less than or equal to a preset threshold, the obtained image data is retained, and the array mb_is_pcm remains unchanged.
  • the above encoding module 120 further includes:
  • the lossy compression coding unit 122 is configured to perform intra prediction and inter prediction on the macroblock to obtain a prediction residual; and perform integer DCT transform on the prediction residual to obtain frequency domain residual data, and The CABAC entropy coding is performed after the frequency domain residual data is quantized.
  • the prediction residual is subjected to integer DCT transform to obtain frequency domain residual data.
  • CABAC entropy coding CABAC entropy coding
  • the cloud desktop content encoding apparatus performs lossy compression encoding on a macroblock of an image class by performing lossless compression encoding on a macroblock of a character class or a graphics class, and performs macroblock on a character class or a graphics class.
  • lossless compression coding if the number of bits of the encoded image data is greater than a preset threshold, the obtained image data is discarded, and the coding type of the macroblock of the character or graphics class is set to the PCM class, which enables A macroblock of a text class or a graphics class does not cause data loss after compression encoding, and can increase the rate of compression encoding.
  • FIG. 9 is a schematic diagram of another refinement module of the encoding module 120 shown in FIG. 7 of the cloud desktop content encoding apparatus of the present disclosure. Based on the example described in FIG. 7 , the encoding module 120 further includes:
  • the Zip compression coding unit 123 is configured to compress and encode the macroblock of the PCM class according to the Zip format to obtain corresponding image data.
  • the PCM-type macroblock After performing compression coding on all macroblocks, it is detected whether there is a macroblock of the PCM type, and if so, the PCM-type macroblock is compression-encoded according to the Zip format to obtain the PCM class. The image data corresponding to the macro block.
  • the array mb_is_pcm contains two elements with values of 1, mb_is_pcm[mb_x1y1], mb_is_pcm[mb_x2y2], and mb_is_pcm[mb_x1y1], mb_is_pcm[mb_x2y2]
  • the corresponding macroblock is compression-encoded according to the Zip format to obtain image data corresponding to the macroblock of the PCM class.
  • the obtained image data corresponding to the macroblock of the PCM class is encapsulated into a NAL packet conforming to the H.264 compression coding standard.
  • the macroblock of the PCM class may be compression-encoded according to the Zip format to obtain corresponding image data.
  • the cloud desktop content encoding apparatus of this example detects whether there is a macroblock of the PCM type after the compression encoding of all the macroblocks, and if so, the macroblock of the PCM type is performed according to the Zip format. Compression coding obtains image data corresponding to macroblocks of the PCM class.
  • the macroblock of the character class or the graphics class is compression-encoded, if the number of bits of the encoded image data is greater than a preset threshold, the obtained image data is discarded, and the encoding method of the Zip format is adopted.
  • the macroblock can be compression-encoded at a higher compression rate and compression speed, which can effectively improve the coding efficiency of the cloud desktop content.
  • FIG. 10 is a block diagram of a second example of the cloud desktop content encoding apparatus of the present disclosure. Based on the example described in FIG. 7 above, in this example, the cloud desktop content encoding apparatus 100 of the present disclosure further includes:
  • the first deblocking filtering module 140 is configured to detect whether the desktop image is a reference image before sending the encoded image data and the macroblock type corresponding to the image data to the client, and if yes, the lossy compression The obtained image data is subjected to deblocking filtering.
  • the encoded image data and the macroblock type corresponding to the image data are sent to the client, whether the desktop image is a reference image is detected, and if so, the image data obtained by lossy compression encoding is obtained. Perform deblocking filtering.
  • the standard H.264 encoding method performs deblocking filtering on all macroblock compression-coded image data, but compresses and encodes the macroblocks of the text or graphics type in the non-continuous tone region.
  • the deblocking filtering of the data will cause significant distortion, so in this example, the image data of the macroblock compression coding of the character class or the graphics class is not subjected to deblocking filtering.
  • the current macroblock and the character class are not Deblocking filtering is performed on the adjacent vertical boundaries of the macroblocks of the graphics class.
  • the obtained image data is subjected to the deblocking filtering process in the horizontal direction, if the macroblock adjacent to the upper side of the current macroblock belongs to a macroblock of a character class or a graphics class, the current macroblock and the character class or the graphics class are not Deblocking filtering is performed on the adjacent horizontal boundaries of the macroblocks.
  • the cloud desktop content encoding apparatus before transmitting the encoded image data and the macroblock type corresponding to the image data to the client, detecting whether the desktop image is a reference image, and if so, the lossy compression coding station
  • the obtained image data is subjected to deblocking filtering, which effectively avoids After the image data is decoded, the resulting desktop image has a square effect problem.
  • FIG. 11 is a block diagram of a first example of a cloud desktop content decoding apparatus of the present disclosure.
  • the cloud desktop content decoding apparatus 200 includes:
  • the receiving module 210 is configured to receive, by the plurality of macroblock compression-encoded image data divided by the desktop image of the cloud desktop, and a macroblock type corresponding to the image data.
  • the client after the client sends the acquisition request to the cloud server, the client receives the image data compressed and encoded by the plurality of macroblocks and the macroblock type corresponding to the image data.
  • the decoding module 220 is configured to: when the macroblock type corresponding to the image data is the first type, decode the image data according to a decoding manner corresponding to the lossless compression encoding, where the macroblock type corresponding to the image data In the case of the second type, the image data is decoded according to a decoding mode corresponding to the lossy compression coding, wherein the first type includes a character class or a graphics class, and the second type includes an image class.
  • the image data is separately decoded according to the macroblock type corresponding to the image data.
  • the macroblock type corresponding to the image data is a character class or a graphic class
  • the image data is not damaged.
  • the decoding method corresponding to the compression coding is decoded, and when the macroblock type corresponding to the image data is an image class, the image data is decoded according to a decoding method corresponding to the lossy compression coding.
  • the desktop image formed by the macroblock generated by decoding the image data is displayed on the client.
  • the decoding module 220 is further configured to:
  • the image data is decoded in accordance with the Zip format.
  • the client determines, after the macroblock compression-encoded image data divided by the desktop image of the cloud desktop, and the macroblock type corresponding to the image data, whether the PCM class exists in the image data.
  • the macroblock compression-encoded image data if present, decodes the image data compression-encoded by the PCM-type macroblock in accordance with the Zip format.
  • the coding type of the macroblock is a PCM class; if it is a non-PCM class, the image data is decoded according to a decoding method corresponding to lossless compression coding; if the macroblock is determined If the coding type is PCM, the image data is not decoded according to the decoding method corresponding to the lossless compression coding, and the image data that is compression-encoded for the next macroblock is decoded.
  • the image data when the image data is decoded, it may also be determined whether the coding type of the macroblock is a PCM class when the image data is encoded by the first type of macroblock compression; if it is a non-PCM class And decoding the image data according to a decoding method corresponding to lossless compression coding; if it is determined that the coding type of the macro block is a PCM class, the image data is decoded according to a Zip format.
  • the client receives the image data of the macroblock compression-encoded by the desktop image of the cloud desktop, and the macroblock type corresponding to the image data
  • the character class The image data of the macroblock compression coding of the graphics type is decoded according to the decoding method corresponding to the lossless compression coding, and the image data of the macroblock compression coding of the image type is decoded according to the decoding method corresponding to the lossy compression coding.
  • the macro block of the text class or the graphic class adopts a decoding mode corresponding to the lossless compression coding, so the client will use the macro block of the text class or the graphic class.
  • the original data can be completely restored without causing any distortion, which solves the problem that the desktop image of the cloud desktop is blurred after the text is sent to the client, thereby improving the user experience.
  • FIG. 12 is a block diagram of a second example of the cloud desktop content decoding apparatus of the present disclosure. Based on the example described in FIG. 11 above, in this example, the cloud desktop content decoding apparatus further includes:
  • the second deblocking filtering module 230 is configured to: after decoding the received image data, detect whether the desktop image formed by the macroblock generated after decoding is a reference image, and if yes, perform the generated macroblock Deblocking filtering.
  • the macroblock generated by decoding the image data corresponding to the macroblock of the image class is subjected to the deblocking filtering process in the horizontal direction, if the macroblock adjacent to the upper side of the current macroblock belongs to a macroblock of a character class or a graphics class, The current macroblock performs deblocking filtering on a horizontal boundary adjacent to the macroblock of the character class or the graphics class.
  • the cloud desktop content decoding apparatus after decoding the received image data compression-encoded by a plurality of macroblocks, detects that the desktop image formed by the macroblock generated after the decoding of the image data is
  • the reference image performs deblocking filtering processing on the macroblock generated after decoding the image data corresponding to the macroblock of the image class, thereby effectively avoiding the problem of square effect of the obtained desktop image after decoding the image data.
  • FIG. 13 is a schematic structural diagram of a cloud desktop content encoding and decoding system of the present disclosure.
  • the cloud desktop content encoding and decoding system includes a cloud desktop content encoding device and a cloud desktop content decoding device, wherein the cloud desktop content encoding device is the cloud desktop content encoding device 100; the cloud desktop content decoding device The device is the above-mentioned cloud desktop content decoding device 200, and details are not described herein again.
  • Embodiments of the present disclosure also provide a computer readable storage medium storing computer executable instructions that, when executed by a processor, implement any of the methods described above.
  • computer storage medium includes volatile and nonvolatile, implemented in any method or technology for storing information, such as computer readable instructions, data structures, program modules or other data. Sex, removable and non-removable media.
  • Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridge, magnetic tape, magnetic disk storage or other magnetic storage device, or may Any other medium used to store the desired information and that can be accessed by the computer.
  • communication media typically includes computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and can include any information delivery media. .
  • the cloud desktop content encoding and decoding method, device and system divide the desktop image of the cloud desktop into a plurality of macroblocks, and then perform lossless compression encoding on the macroblocks of the text class or the graphics class, and the image class
  • the macroblock performs lossy compression coding, and sends the encoded image data and the macroblock type corresponding to the image data to the client; in addition, the client receives the image data and the macroblock corresponding to the image data.
  • the image data of the macroblock compression coding of the character class or the graphics class is decoded according to the decoding method corresponding to the lossless compression coding, and the image data corresponding to the macroblock compression coding of the image class is decoded according to the lossy compression coding.
  • the macro file of the character class or the graphic class adopts a decoding method corresponding to lossless compression coding and lossless compression coding, so the client will use the character class.
  • the original data can be completely restored without causing any distortion, and the text or graphic display mode of the desktop image of the cloud desktop is sent to the client.
  • the problem of paste has improved the user experience.
  • the present disclosure therefore has industrial applicability.

Landscapes

  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Signal Processing (AREA)
  • Compression Or Coding Systems Of Tv Signals (AREA)
  • Compression Of Band Width Or Redundancy In Fax (AREA)

Abstract

公开了一种云桌面内容编码与解码方法，方法为：将云桌面的桌面图像按照预设的方式划分为多个宏块，并分别确定每个宏块对应的宏块类型，将文字类或图形类的宏块进行无损压缩编码，将图像类的宏块进行有损压缩编码，并将压缩编码后的图像数据以及所述图像数据对应的宏块类型发送至客户端，客户端根据接收到的宏块类型分别对所述图像数据进行解码。还公开了一种云桌面内容编码与解码装置、系统。

Description

云桌面内容编码与解码方法及装置、系统 技术领域
本公开涉及图像压缩编码与解码技术领域，尤其涉及一种云桌面内容编码与解码方法及装置、系统。
背景技术
随着移动互联网的迅猛发展，云桌面这项应用已经被越来越多的人们所使用。利用云桌面这项应用，用户可以通过PC、笔记本、上网本、Pad、手机等一切可以连接网络的终端设备来进行办公，进入移动办公新时代。
包含云桌面内容的桌面图像是一种混合图像，其中包含了一部分非连续色调变化区域，如文字，图标，图形，还包含了一部分连续色调变化区域，如自然图像，视频等。标准的H.264编码器在对连续色调变化的图像序列进行编码，会有较好的编码效果，但是在对文字或图形区域进行编码时，由于会引入编码损失，使解码后的文字或图形显示有边缘毛刺，失真等现象。H.264编码器能利用空域时域的相关性，提升压缩效率，但是如果直接对桌面图像进行编码的话，会导致解码后的文字或图形显示模糊或失真，影响用户的使用体验。
发明内容
以下是对本文详细描述的主题的概述。本概述并非是为了限制权利要求的保护范围。
本公开提出了一种云桌面内容编码与解码方法及装置、系统。
本公开提供一种云桌面内容编码方法，所述方法包括：
将云桌面的桌面图像按照预设的方式划分为多个宏块，并确定所述多个宏块分别对应的宏块类型；
在所述宏块类型为第一类型的情况下，对所述宏块进行无损压缩编码，在所述宏块类型为第二类型的情况下，对所述宏块进行有损压缩编码，其中， 所述第一类型包括文字类或图形类，所述第二类型包括图像类；
将编码得到的图像数据及所述图像数据对应的宏块类型发送至客户端。
在示例性实施例中，对所述宏块进行无损压缩编码的步骤包括：
对所述宏块进行帧内预测和帧间预测得到预测残差；
将所述预测残差按照预设的压缩方法进行变换，并将变换后的预测残差重新排序后进行基于上下文的自适应二进制算术CABAC熵编码。
在示例性实施例中，对所述宏块进行无损压缩编码之后还包括：
若编码得到的图像数据的比特数大于预设的阈值，则丢弃得到的图像数据，并将所述宏块的编码类型设置为脉冲编码调制PCM类；
若编码得到的图像数据的比特数小于或等于预设的阈值，则保留得到的图像数据。
在示例性实施例中，在将所述宏块的编码类型设置为PCM类之后还包括：
将所述PCM类的宏块按照Zip格式进行压缩编码，得到对应的图像数据。
在示例性实施例中，对所述宏块进行有损压缩编码的步骤包括：
对所述宏块进行帧内预测和帧间预测得到预测残差；
将所述预测残差进行整数离散余弦变换DCT，得到频域残差数据，并将所述频域残差数据量化后进行所述CABAC熵编码。
在示例性实施例中，将编码得到的图像数据及所述图像数据对应的宏块类型发送至客户端之前还包括：
检测所述桌面图像是否为参考图像，若是，则对有损压缩编码所得到的图像数据进行去块效应滤波。
此外，本公开还提供一种云桌面内容解码方法，所述方法包括：
接收由云桌面的桌面图像划分的多个宏块压缩编码后的图像数据，以及所述图像数据对应的宏块类型；
在所述图像数据对应的宏块类型为第一类型的情况下，对所述图像数据 按照无损压缩编码对应的解码方式进行解码，在所述图像数据对应的宏块类型为第二类型的情况下，对所述图像数据按照有损压缩编码对应的解码方式进行解码，其中，所述第一类型包括文字类或图形类，所述第二类型包括图像类。
在示例性实施例中，所述接收由云桌面的桌面图像划分的多个宏块压缩编码后的图像数据，以及所述图像数据对应的宏块类型之后还包括：
在所述图像数据对应的宏块类型为PCM类的情况下，对所述图像数据按照Zip格式进行解码。
在示例性实施例中，所述云桌面内容解码方法还包括：
在对接收到的图像数据进行解码后，检测解码后产生的宏块所构成的桌面图像是否为参考图像，若是，则对产生的所述宏块进行去块效应滤波。
此外，本公开还提供一种云桌面内容编码装置，所述装置包括：
分类模块，配置为将云桌面的桌面图像按照预设的方式划分为多个宏块，并确定所述多个宏块分别对应的宏块类型；
编码模块，配置为在所述宏块类型为第一类型的情况下，对所述宏块进行无损压缩编码，在所述宏块类型为第二类型的情况下，对所述宏块进行有损压缩编码，其中，所述第一类型包括文字类或图形类，所述第二类型包括图像类；
传输模块，配置为将编码得到的图像数据及所述图像数据对应的宏块类型发送至客户端。
在示例性实施例中，所述编码模块包括：
无损压缩编码单元，配置为对所述宏块进行帧内预测和帧间预测得到预测残差；以及将所述预测残差按照预设的压缩方法进行变换，并将变换后的预测残差重新排序后进行基于上下文的自适应二进制算术CABAC熵编码。
在示例性实施例中，所述无损压缩编码单元还配置为：
若编码得到的图像数据的比特数大于预设的阈值，则丢弃得到的图像数据，并将所述宏块的编码类型设置为脉冲编码调制PCM类；
若编码得到的图像数据的比特数小于或等于预设的阈值，则保留得到的图像数据。
在示例性实施例中，所述编码模块还包括：
Zip压缩编码单元，配置为将所述PCM类的宏块按照Zip格式进行压缩编码，得到对应的图像数据。
在示例性实施例中，所述编码模块还包括：
有损压缩编码单元，配置为对所述宏块进行帧内预测和帧间预测得到预测残差；以及将所述预测残差进行整数DCT变换，得到频域残差数据，并将所述频域残差数据量化后进行所述CABAC熵编码。
在示例性实施例中，所述云桌面内容编码装置还包括：
第一去块效应滤波模块，配置为将编码得到的图像数据及所述图像数据对应的宏块类型发送至客户端之前，检测所述桌面图像是否为参考图像，若是，则对有损压缩编码所得到的图像数据进行去块效应滤波。
此外，本公开还提供一种云桌面内容解码装置，所述装置包括：
接收模块，配置为接收由云桌面的桌面图像划分的多个宏块压缩编码后的图像数据，以及所述图像数据对应的宏块类型；
解码模块，配置为在所述图像数据对应的宏块类型为第一类型的情况下，对所述图像数据按照无损压缩编码对应的解码方式进行解码，在所述图像数据对应的宏块类型为第二类型的情况下，对所述图像数据按照有损压缩编码对应的解码方式进行解码，其中，所述第一类型包括文字类或图形类，所述第二类型包括图像类。
在示例性实施例中，所述解码模块还配置为：
在所述图像数据对应的宏块类型为PCM类的情况下，对所述图像数据按照Zip格式进行解码。
在示例性实施例中，所述云桌面内容解码装置还包括：
第二去块效应滤波模块，配置为在对接收到的图像数据进行解码后，检测解码后产生的宏块所构成的桌面图像是否为参考图像，若是，则对产生的 所述宏块进行去块效应滤波。
此外，本公开还提供一种云桌面内容编码与解码系统，所述系统包括云桌面内容编码装置与云桌面内容解码装置，其中，所述云桌面内容编码装置为上述云桌面内容编码装置，所述云桌面内容解码装置为上述云桌面内容解码装置。
本公开实施例还提供了一种计算机可读存储介质，存储有计算机可执行指令，所述计算机可执行指令被处理器执行时实现以上描述的任一方法。
本公开所提出的云桌面内容编码与解码方法及装置、系统，通过将云桌面的桌面图像划分为多个宏块，然后对文字类或图形类的宏块进行无损压缩编码，对图像类的宏块进行有损压缩编码，并将编码后的图像数据及所述图像数据对应的宏块类型发送至客户端；另外，客户端在接收到所述图像数据及所述图像数据对应的宏块类型后，对文字类或图形类的宏块压缩编码后的图像数据按照无损压缩编码对应的解码方法进行解码，对图像类的宏块压缩编码后的图像数据按照有损压缩编码对应的解码方法进行解码。本公开在对云桌面的桌面图像进行编码与解码的过程中，由于对文字类或图形类的宏块采用的是无损压缩编码及无损压缩编码对应的解码方式，故客户端将所述文字类或图形类的宏块对应的图像数据进行解码后，可完全恢复原始数据而不引起任何失真，解决了云桌面的桌面图像发送到客户端后文字或图形显示模糊的问题，提升了用户使用体验。
在阅读并理解了附图和详细描述后，可以明白其他方面。
附图说明
图1是本公开云桌面内容编码方法第一示例的流程示意图；
图2是本公开云桌面内容编码方法图1所示步骤S120的细化步骤示意图；
图3是本公开云桌面内容编码方法图1所示步骤S120的另一细化步骤示意图；
图4是本公开云桌面内容编码方法第二示例的流程示意图；
图5是本公开云桌面内容解码方法第一示例的流程示意图；
图6是本公开云桌面内容解码方法第二示例的流程示意图；
图7是本公开云桌面内容编码装置第一示例的模块示意图；
图8是本公开云桌面内容编码装置图7所示编码模块120的细化模块示意图；
图9是本公开云桌面内容编码装置图7所示编码模块120的另一细化模块示意图；
图10是本公开云桌面内容编码装置第二示例的模块示意图；
图11是本公开云桌面内容解码装置第一示例的模块示意图；
图12是本公开云桌面内容解码装置第二示例的模块示意图；
图13是本公开云桌面内容编码与解码系统的结构示意图。
具体实施方式
以下结合说明书附图对本公开的示例性实施例进行说明，应当理解，此处所描述的示例性实施例仅用于说明和解释本公开，并不用于限定本公开。
参照图1，图1是本公开云桌面内容编码方法第一示例的流程示意图。本示例中，所述云桌面内容编码方法包括：
步骤S110，将云桌面的桌面图像按照预设的方式划分为多个宏块，并确定所述多个宏块分别对应的宏块类型。
本示例中，所述云桌面的内容是指用户在云服务器请求获取的内容，例如文字、图形、图像或视频等，且云桌面的内容一般是由许多幅桌面图像构成的。为了提高传输速率，云服务器在将云桌面的内容发送至客户端之前，要将所述云桌面的内容进行压缩编码。本步骤中，在将所述云桌面的内容进行压缩编码之前，先将每一幅桌面图像划分为多个宏块，每个宏块包括16×16个像素，然后确定每个宏块分别对应的宏块类型，所述宏块主要可以分为文字类、图形类、图像类。
步骤S120，在所述宏块类型为第一类型的情况下，对所述宏块进行无 损压缩编码，在所述宏块类型为第二类型的情况下，对所述宏块进行有损压缩编码，其中，所述第一类型包括文字类或图形类，所述第二类型包括图像类。
本示例中，对文字类或图形类的宏块进行无损压缩编码，对图像类的宏块进行有损压缩编码。其中，无损压缩编码是利用数据的冗余进行压缩，可完全恢复原始数据而不引起任何失真，比较适合文字类或图形类的宏块。有损压缩编码是利用了人类对图像中的某些频率成分不敏感的特性，允许压缩过程中损失一定的信息，虽然不能完全恢复原始数据，但是所损失的部分对理解原始图像的影响较小，且压缩比较高，比较适合于图像类的宏块。
其中，上述有损压缩编码及无损压缩编码均可基于H.264压缩编码标准。
步骤S130，将编码得到的图像数据及所述图像数据对应的宏块类型发送至客户端。
本示例中，在对所述多个宏块压缩编码完成以后，将所述多个宏块压缩编码后的图像数据及所述图像数据分别对应的宏块类型同时发送至客户端。
本示例所述的云桌面内容编码方法，通过将云桌面的桌面图像划分为多个宏块，然后对文字类或图形类的宏块进行无损压缩编码，对图像类的宏块进行有损压缩编码，并将编码后的图像数据及所述图像数据对应的宏块类型发送至客户端，由于在对云桌面的桌面图像进行编码时，对文字类或图形类的宏块采用的是无损压缩编码，故客户端将所述文字类或图形类的宏块对应的图像数据进行解码后，可完全恢复原始数据而不引起任何失真，解决了云桌面的桌面图像发送到客户端后文字或图形显示模糊的问题，提升了用户使用体验。
参照图2，图2是本公开云桌面内容编码方法图1所示步骤S120的细化步骤示意图。基于上述图1所述的示例，本示例中，上述步骤S120中对所述宏块进行无损压缩编码的步骤包括：
步骤S121，对所述宏块进行帧内预测和帧间预测得到预测残差；
步骤S122，将所述预测残差按照预设的压缩方法进行变换，并将变换 后的预测残差重新排序后进行基于上下文的自适应二进制算术CABAC熵编码。
其中，在所述宏块类型为第一类型的情况下，对所述第一类型的宏块进行帧内预测和帧间预测得到预测残差，然后可采用Transform_bypass类型的变换方式对所述预测残差进行变换，且不对所述预测参差进行频域的变换和量化，从而避免造成编码损失。将变换后的预测残差重新排序后进行基于上下文的自适应二进制算术编码，即CABAC(Context-based Adaptive Binary Arithmetic Coding)熵编码，得到所述第一类型的宏块压缩编码后的图像数据。
在对所述宏块进行无损压缩编码之后还包括：
若编码得到的图像数据的比特数大于预设的阈值，则丢弃得到的图像数据，并将所述宏块的编码类型设置为脉冲编码调制PCM类；若编码得到的图像数据的比特数小于或等于预设的阈值，则保留得到的图像数据。
其中，在对所述宏块进行无损压缩编码之后，判断编码得到的图像数据的比特数是否大于预设的阈值，若大于，则放弃得到的图像数据，并将所述宏块的编码类型设置为PCM类，相当于对比特数大于预设阈值的图像数据添加一个编码标签；若编码得到的图像数据的bit数小于或等于预设的阈值，则保留得到的图像数据。
本示例中，在将云桌面的桌面图像按照预设的方式划分为多个宏块后，创建一个大小与所述多个宏块的个数相同的数组mb_is_pcm。在对所述多个宏块进行压缩编码前，数组mb_is_pcm中的所有元素会被初始化为0。在所述宏块类型为第一类型的情况下，若对所述宏块进行无损压缩编码后的得到的图像数据的比特数大于预设的阈值，则放弃得到的图像数据，并将所述宏块的编码类型设置为PCM类，以及将数组mb_is_pcm[mb_xy]设置为1，其中，“mb_xy”为所述编码类型为PCM类的宏块的索引信息。若编码得到的图像数据的比特数小于或等于预设的阈值，则保留得到的图像数据，数组mb_is_pcm保持不变。
参照图3，图3是本公开云桌面内容编码方法图1所示步骤S120的另一细化步骤示意图。基于上述图1所述的示例，本示例中，上述步骤S120 中对所述宏块进行有损压缩编码的步骤包括：
步骤S123，对所述宏块进行帧内预测和帧间预测得到预测残差；
步骤S124，将所述预测残差进行整数DCT变换，得到频域残差数据，并将所述频域残差数据量化后进行所述CABAC熵编码。
其中，在所述宏块类型为第二类型的情况下，对所述宏块进行帧内预测和帧间预测得到预测残差后，将预测残差进行整数DCT变换，得到频域残差数据，根据码率控制得到的量化系数对所述频域残差数据进行量化，并且对量化后的数据重新排序后进行基于上下文的自适应二进制算术编码，即CABAC熵编码，得到所述宏块压缩编码后的图像数据。
本示例所述的云桌面内容编码方法，通过对文字类或图形类的宏块进行无损压缩编码，对图像类的宏块进行有损压缩编码，且在对文字类或图形类的宏块进行无损压缩编码后，若编码得到的图像数据的比特数大于预设的阈值，则放弃得到的图像数据，并将所述文字类或图形类的宏块的编码类型设置为PCM类，既能够让文字类或图形类的宏块在压缩编码后不会造成数据损失，又可以提高压缩编码的速率。
基于上述图1与图2所述的示例，本示例中，在将所述宏块的编码类型设置为PCM类之后还包括：
将所述PCM类的宏块按照Zip格式进行压缩编码，得到对应的图像数据。
其中，在对所有的宏块进行压缩编码之后，检测是否存在编码类型为PCM类的宏块，若存在，则将所述PCM类的宏块按照Zip格式进行压缩编码，得到所述PCM类的宏块对应的图像数据。
例如，在对所有的宏块进行压缩编码之后，根据数组mb_is_pcm的值，判断是否存在编码类型为PCM类的宏块，若存在，则将所述PCM类的宏块按照Zip格式进行压缩编码。例如，在对所有的宏块进行压缩编码之后，检测到数组mb_is_pcm中包含2个值为1的元素，分别为mb_is_pcm[mb_x1y1]、mb_is_pcm[mb_x2y2]，则将mb_is_pcm[mb_x1y1]、mb_is_pcm[mb_x2y2]对应的宏块按照Zip格式进行压缩编码，得到所述PCM 类的宏块对应的图像数据。并且将得到的所述PCM类的宏块对应的图像数据封装成符合H.264压缩编码标准的NAL包。
另外，本示例中，也可以在将所述宏块的编码类型设置为PCM类之后，便将所述PCM类的宏块按照Zip格式进行压缩编码，得到对应的图像数据。
本示例所述的云桌面内容编码方法，在对所有的宏块进行压缩编码之后，检测是否存在编码类型为PCM类的宏块，若存在，则将所述PCM类的宏块按照Zip格式进行压缩编码，得到所述PCM类的宏块对应的图像数据。即本示例中，在对文字类或图形类的宏块进行压缩编码时，若编码得到的图像数据的比特数大于预设的阈值，则放弃得到的图像数据，而采用Zip格式的编码方式，使得该宏块能够以较高的压缩率和压缩速度进行压缩编码，能够有效提高云桌面内容的编码效率。
参照图4，图4是本公开云桌面内容编码方法第二示例的流程示意图。基于上述图1所述的示例，本示例中，本公开云桌面内容编码方法还包括：
步骤S140，检测所述桌面图像是否为参考图像，若是，则对有损压缩编码所得到的图像数据进行去块效应滤波。
本示例中，在将编码得到的图像数据及所述图像数据对应的宏块类型发送至客户端之前，检测所述桌面图像是否为参考图像，若是，则对有损压缩编码所得到的图像数据进行去块效应滤波。
其中，标准的H.264编码方法中是会对所有的宏块压缩编码后的图像数据进行去块效应滤波的，但是对于非连续色调区域的文字类或图形类的宏块压缩编码后的图像数据进行去块效应滤波反而会造成明显的失真，所以本示例中对文字类或图形类的宏块压缩编码后的图像数据不进行去块效应滤波。
例如，对得到的图像数据进行竖直方向的去块效应滤波处理时，若当前宏块的左边相邻的宏块属于文字类或图形类的宏块，则不对当前宏块与所述文字类或图形类的宏块相邻的竖直边界进行去块效应滤波。对得到的图像数据进行水平方向的去块效应滤波处理时，若当前宏块的上边相邻的宏块属于文字类或图形类的宏块，则不对当前宏块与所述文字类或图形类的宏块相邻的水平边界进行去块效应滤波。
本示例所述的云桌面内容编码方法，将编码得到的图像数据及所述图像数据对应的宏块类型发送至客户端之前，检测桌面图像是否为参考图像，若是，则对有损压缩编码所得到的图像数据进行去块效应滤波，有效避免了在所述图像数据进行解码之后，得到的桌面图像出现方块效应的问题。
参照图5，图5是本公开云桌面内容解码方法第一示例的流程示意图。本示例中，所述云桌面内容解码方法包括：
步骤S210，接收由云桌面的桌面图像划分的多个宏块压缩编码后的图像数据，以及所述图像数据对应的宏块类型。
本示例中，客户端在向云服务器发送获取请求后，同时接收云服务器发送的由多个宏块经过压缩编码后的图像数据以及所述图像数据对应的宏块类型。
步骤S220，在所述图像数据对应的宏块类型为第一类型的情况下，对所述图像数据按照无损压缩编码对应的解码方式进行解码，在所述图像数据对应的宏块类型为第二类型的情况下，对所述图像数据按照有损压缩编码对应的解码方式进行解码，其中，所述第一类型包括文字类或图形类，所述第二类型包括图像类。
本示例中，根据所述图像数据对应的宏块类型分别对所述图像数据进行解码，例如，在所述图像数据对应的宏块类型为文字类或图形类时，对所述图像数据按照无损压缩编码对应的解码方式进行解码，在所述图像数据对应的宏块类型为图像类时，对所述图像数据按照有损压缩编码对应的解码方式进行解码。
其中，在对所述图像数据进行解码之后，将由所述图像数据解码后产生的宏块所构成的桌面图像在客户端进行显示。
本示例中，客户端在接收到由云桌面的桌面图像划分的多个宏块压缩编码后的图像数据，以及所述图像数据对应的宏块类型之后还包括：
在所述图像数据对应的宏块类型为PCM类的情况下，对所述图像数据按照Zip格式进行解码。
其中，客户端在接收到由云桌面的桌面图像划分的多个宏块压缩编码后 的图像数据，以及所述图像数据对应的宏块类型之后，判断所述图像数据中是否存在由PCM类的宏块压缩编码后的图像数据，若存在，则将所述由PCM类的宏块压缩编码后的图像数据按照Zip格式进行解码。
其中，在对所述图像数据进行解码时，若遇到由第一类型的宏块压缩编码后的图像数据，则确定所述宏块的编码类型是否为PCM类；若为非PCM类，则将所述图像数据按照无损压缩编码对应的解码方法进行解码；若确定所述宏块的编码类型为PCM类，则不对所述图像数据按照无损压缩编码对应的解码方法进行解码，跳至对下一个宏块压缩编码后的图像数据进行解码。当对最后一个宏块压缩编码后的图像数据进行解码之后，判断所述图像数据中是否存在由PCM类的宏块压缩编码后的图像数据，若存在，则将所述由PCM类的宏块压缩编码后的图像数据按照Zip格式进行解码。
另外，在对所述图像数据进行解码时，还可以在遇到由第一类型的宏块压缩编码后的图像数据时，判断所述宏块的编码类型是否为PCM类；若为非PCM类，则将所述图像数据按照无损压缩编码对应的解码方法进行解码；若确定所述宏块的编码类型为PCM类，则将所述图像数据按照Zip格式进行解码。
本示例所述的云桌面内容解码方法，客户端在接收到由云桌面的桌面图像划分的多个宏块压缩编码后的图像数据，以及所述图像数据对应的宏块类型后，对文字类或图形类的宏块压缩编码后的图像数据按照无损压缩编码对应的解码方法进行解码，对图像类的宏块压缩编码后的图像数据按照有损压缩编码对应的解码方法进行解码。本公开在对云桌面的桌面图像进行解码的过程中，由于对文字类或图形类的宏块采用的是无损压缩编码对应的解码方式，故客户端将所述文字类或图形类的宏块对应的图像数据进行解码后，可完全恢复原始数据而不引起任何失真，解决了云桌面的桌面图像发送到客户端后文字或图形显示模糊的问题，提升了用户使用体验。
参照图6，图6是本公开云桌面内容解码方法第二示例的流程示意图。基于上述图5所述的示例，本示例中，所述云桌面内容解码方法还包括：
步骤S230，在对接收到的图像数据进行解码后，检测解码后产生的宏块所构成的桌面图像是否为参考图像，若是，则对产生的所述宏块进行去块 效应滤波。
其中，在对接收到的图像数据进行解码后，若检测到解码后产生的宏块所构成的桌面图像为参考图像时，则仅对图像类的宏块对应的图像数据解码后产生的宏块进行去块效应滤波。其中，对所述宏块进行竖直方向的去块效应滤波处理时，若当前宏块的左边相邻的宏块属于文字类或图形类的宏块，则不对当前宏块与所述文字类或图形类的宏块相邻的竖直边界进行去块效应滤波。对图像类的宏块对应的图像数据解码后产生的宏块进行水平方向的去块效应滤波处理时，若当前宏块的上边相邻的宏块属于文字类或图形类的宏块，则不对当前宏块与所述文字类或图形类的宏块相邻的水平边界进行去块效应滤波。
本示例所述的云桌面内容解码方法，在对接收到的由多个宏块压缩编码后的图像数据进行解码后，若检测到所述图像数据解码后产生的宏块所构成的桌面图像为参考图像，对图像类的宏块对应的图像数据解码后所产生的宏块进行去块效应滤波处理，有效避免了在对所述图像数据进行解码之后，得到的桌面图像出现方块效应的问题。
参照图7，图7是本公开云桌面内容编码装置第一示例的模块示意图。本示例中，所述云桌面内容编码装置100包括：
分类模块110，配置为将云桌面的桌面图像按照预设的方式划分为多个宏块，并确定所述多个宏块分别对应的宏块类型。
本示例中，所述云桌面的内容是指用户在云服务器请求获取的内容，例如文字、图形、图像或视频等，且云桌面的内容一般是由许多幅桌面图像构成的。为了提高传输速率，云服务器在将云桌面的内容发送至客户端之前，要将所述云桌面的内容进行压缩编码。本步骤中，在将所述云桌面的内容进行压缩编码之前，先将每一幅桌面图像划分为多个宏块，每个宏块包括16×16个像素，然后确定每个宏块分别对应的宏块类型，所述宏块主要可以分为文字类、图形类、图像类。
编码模块120，配置为在所述宏块类型为第一类型的情况下，对所述宏块进行无损压缩编码，在所述宏块类型为第二类型的情况下，对所述宏块进行有损压缩编码，其中，所述第一类型包括文字类或图形类，所述第二类型 包括图像类。
本示例中，对文字类或图形类的宏块进行无损压缩编码，对图像类的宏块进行有损压缩编码。其中，无损压缩编码是利用数据的冗余进行压缩，可完全恢复原始数据而不引起任何失真，比较适合文字类或图形类的宏块。有损压缩编码是利用了人类对图像中的某些频率成分不敏感的特性，允许压缩过程中损失一定的信息，虽然不能完全恢复原始数据，但是所损失的部分对理解原始图像的影响较小，且压缩比较高，比较适合于图像类的宏块。
其中，上述有损压缩编码及无损压缩编码均可基于H.264压缩编码标准。
传输模块130，配置为将编码得到的图像数据及所述图像数据对应的宏块类型发送至客户端。
本示例中，在对所述多个宏块压缩编码完成以后，将所述多个宏块压缩编码后的图像数据及所述图像数据分别对应的宏块类型发送至客户端。
本示例所述的云桌面内容编码装置，通过将云桌面的桌面图像划分为多个宏块，然后对文字类或图形类的宏块进行无损压缩编码，对图像类的宏块进行有损压缩编码，并将编码后的图像数据及所述图像数据对应的宏块类型发送至客户端，由于在对云桌面的桌面图像进行编码时，对文字类或图形类的宏块采用的是无损压缩编码，故客户端将所述文字类或图形类的宏块对应的图像数据进行解码后，可完全恢复原始数据而不引起任何失真，解决了云桌面的桌面图像发送到客户端后文字或图形显示模糊的问题，提升了用户使用体验。
参照图8，图8是本公开云桌面内容编码装置图7所示编码模块120的细化模块示意图。基于上述图7所述的示例，本示例中，上述编码模块120包括：
无损压缩编码单元121，配置为对所述宏块进行帧内预测和帧间预测得到预测残差；以及将所述预测残差按照预设的压缩方法进行变换，并将变换后的预测残差重新排序后进行基于上下文的自适应二进制算术CABAC熵编码。
其中，在所述宏块类型为第一类型的情况下，对所述第一类型的宏块进行帧内预测和帧间预测得到预测残差，然后可采用Transform_bypass类型的变换方式对所述预测残差进行变换，且不对所述预测参差进行频域的变换和量化，从而避免造成编码损失。将变换后的预测残差重新排序后进行基于上下文的自适应二进制算术编码，即CABAC(Context-based Adaptive Binary Arithmetic Coding)熵编码，得到所述第一类型的宏块压缩编码后的图像数据。
上述无损压缩编码单元121还配置为：
若编码得到的图像数据的比特数大于预设的阈值，则丢弃得到的图像数据，并将所述宏块的编码类型设置为脉冲编码调制PCM类；若编码得到的图像数据的比特数小于或等于预设的阈值，则保留得到的图像数据。
其中，在对所述宏块进行无损压缩编码之后，判断编码得到的图像数据的比特数是否大于预设的阈值，若大于，则放弃得到的图像数据，并将所述宏块的编码类型设置为PCM类，相当于对比特数大于预设阈值的图像数据添加一个编码标签；若编码得到的图像数据的bit数小于或等于预设的阈值，则保留得到的图像数据。
本示例中，在将云桌面的桌面图像按照预设的方式划分为多个宏块后，创建一个大小与所述多个宏块的个数相同的数组mb_is_pcm。在对所述多个宏块进行压缩编码前，数组mb_is_pcm中的所有元素会被初始化为0。在所述宏块类型为第一类型的情况下，若对所述宏块进行无损压缩编码后的得到的图像数据的比特数大于预设的阈值，则放弃得到的图像数据，并将所述宏块的编码类型设置为PCM类，以及将数组mb_is_pcm[mb_xy]设置为1，其中，“mb_xy”为所述编码类型为PCM类的宏块的索引信息。若编码得到的图像数据的比特数小于或等于预设的阈值，则保留得到的图像数据，数组mb_is_pcm保持不变。
上述编码模块120还包括：
有损压缩编码单元122，配置为对所述宏块进行帧内预测和帧间预测得到预测残差；以及将所述预测残差进行整数DCT变换，得到频域残差数据，并将所述频域残差数据量化后进行所述CABAC熵编码。
其中，在所述宏块类型为第二类型的情况下，对所述宏块进行帧内预测和帧间预测得到预测残差后，将预测残差进行整数DCT变换，得到频域残差数据，根据码率控制得到的量化系数对所述频域残差数据进行量化，并且对量化后的数据重新排序后进行基于上下文的自适应二进制算术编码，即CABAC熵编码，得到所述宏块压缩编码后的图像数据。
本示例所述的云桌面内容编码装置，通过对文字类或图形类的宏块进行无损压缩编码，对图像类的宏块进行有损压缩编码，且在对文字类或图形类的宏块进行无损压缩编码后，若编码得到的图像数据的比特数大于预设的阈值，则放弃得到的图像数据，并将所述文字类或图形类的宏块的编码类型设置为PCM类，既能够让文字类或图形类的宏块在压缩编码后不会造成数据损失，又可以提高压缩编码的速率。
参照图9，图9是本公开云桌面内容编码装置图7所示编码模块120的另一细化模块示意图，基于上述图7所述的示例，本示例中，所述编码模块120还包括：
Zip压缩编码单元123，配置为将所述PCM类的宏块按照Zip格式进行压缩编码，得到对应的图像数据。
其中，在对所有的宏块进行压缩编码之后，检测是否存在编码类型为PCM类的宏块，若存在，则将所述PCM类的宏块按照Zip格式进行压缩编码，得到所述PCM类的宏块对应的图像数据。
例如，在对所有的宏块进行压缩编码之后，根据数组mb_is_pcm的值，判断是否存在编码类型为PCM类的宏块，若存在，则将所述PCM类的宏块按照Zip格式进行压缩编码。例如，在对所有的宏块进行压缩编码之后，检测到数组mb_is_pcm中包含2个值为1的元素，分别为mb_is_pcm[mb_x1y1]、mb_is_pcm[mb_x2y2]，则将mb_is_pcm[mb_x1y1]、mb_is_pcm[mb_x2y2]对应的宏块按照Zip格式进行压缩编码，得到所述PCM类的宏块对应的图像数据。并且将得到的所述PCM类的宏块对应的图像数据封装成符合H.264压缩编码标准的NAL包。
另外，本示例中，也可以在将所述宏块的编码类型设置为PCM类之后，便将所述PCM类的宏块按照Zip格式进行压缩编码，得到对应的图像数据。
本示例所述的云桌面内容编码装置，在对所有的宏块进行压缩编码之后，检测是否存在编码类型为PCM类的宏块，若存在，则将所述PCM类的宏块按照Zip格式进行压缩编码，得到所述PCM类的宏块对应的图像数据。即本示例中，在对文字类或图形类的宏块进行压缩编码时，若编码得到的图像数据的比特数大于预设的阈值，则放弃得到的图像数据，而采用Zip格式的编码方式，使得该宏块能够以较高的压缩率和压缩速度进行压缩编码，能够有效提高云桌面内容的编码效率。
参照图10，图10是本公开云桌面内容编码装置第二示例的模块示意图。基于上述图7所述的示例，本示例中，本公开云桌面内容编码装置100还包括：
第一去块效应滤波模块140，配置为将编码得到的图像数据及所述图像数据对应的宏块类型发送至客户端之前，检测所述桌面图像是否为参考图像，若是，则对有损压缩编码所得到的图像数据进行去块效应滤波。
本示例中，在将编码得到的图像数据及所述图像数据对应的宏块类型发送至客户端之前，检测所述桌面图像是否为参考图像，若是，则对有损压缩编码所得到的图像数据进行去块效应滤波。
其中，标准的H.264编码方法中是会对所有的宏块压缩编码后的图像数据进行去块效应滤波的，但是对于非连续色调区域的文字类或图形类的宏块压缩编码后的图像数据进行去块效应滤波反而会造成明显的失真，所以本示例中对文字类或图形类的宏块压缩编码后的图像数据不进行去块效应滤波。
例如，对得到的图像数据进行竖直方向的去块效应滤波处理时，若当前宏块的左边相邻的宏块属于文字类或图形类的宏块，则不对当前宏块与所述文字类或图形类的宏块相邻的竖直边界进行去块效应滤波。对得到的图像数据进行水平方向的去块效应滤波处理时，若当前宏块的上边相邻的宏块属于文字类或图形类的宏块，则不对当前宏块与所述文字类或图形类的宏块相邻的水平边界进行去块效应滤波。
本示例所述的云桌面内容编码装置，将编码得到的图像数据及所述图像数据对应的宏块类型发送至客户端之前，检测桌面图像是否为参考图像，若是，则对有损压缩编码所得到的图像数据进行去块效应滤波，有效避免了在 所述图像数据进行解码之后，得到的桌面图像出现方块效应的问题。
参照图11，图11是本公开云桌面内容解码装置第一示例的模块示意图。本示例中，所述云桌面内容解码装置200包括：
接收模块210，配置为接收由云桌面的桌面图像划分的多个宏块压缩编码后的图像数据，以及所述图像数据对应的宏块类型。
本示例中，客户端在向云服务器发送获取请求后，同时接收云服务器发送的由多个宏块经过压缩编码后的图像数据以及所述图像数据对应的宏块类型。
解码模块220，配置为在所述图像数据对应的宏块类型为第一类型的情况下，对所述图像数据按照无损压缩编码对应的解码方式进行解码，在所述图像数据对应的宏块类型为第二类型的情况下，对所述图像数据按照有损压缩编码对应的解码方式进行解码，其中，所述第一类型包括文字类或图形类，所述第二类型包括图像类。
本示例中，根据所述图像数据对应的宏块类型分别对所述图像数据进行解码，例如，在所述图像数据对应的宏块类型为文字类或图形类时，对所述图像数据按照无损压缩编码对应的解码方式进行解码，在所述图像数据对应的宏块类型为图像类时，对所述图像数据按照有损压缩编码对应的解码方式进行解码。
其中，在对所述图像数据进行解码之后，将由所述图像数据解码后产生的宏块所构成的桌面图像在客户端进行显示。
本示例中，所述解码模块220还配置为：
在所述图像数据对应的宏块类型为PCM类的情况下，对所述图像数据按照Zip格式进行解码。
其中，客户端在接收到由云桌面的桌面图像划分的多个宏块压缩编码后的图像数据，以及所述图像数据对应的宏块类型之后，判断所述图像数据中是否存在由PCM类的宏块压缩编码后的图像数据，若存在，则将所述由PCM类的宏块压缩编码后的图像数据按照Zip格式进行解码。
其中，在对所述图像数据进行解码时，若遇到由第一类型的宏块压缩编 码后的图像数据，则确定所述宏块的编码类型是否为PCM类；若为非PCM类，则将所述图像数据按照无损压缩编码对应的解码方法进行解码；若确定所述宏块的编码类型为PCM类，则不对所述图像数据按照无损压缩编码对应的解码方法进行解码，跳至对下一个宏块压缩编码后的图像数据进行解码。当对最后一个宏块压缩编码后的图像数据进行解码之后，判断所述图像数据中是否存在由PCM类的宏块压缩编码后的图像数据，若存在，则将所述由PCM类的宏块压缩编码后的图像数据按照Zip格式进行解码。
另外，在对所述图像数据进行解码时，还可以在遇到由第一类型的宏块压缩编码后的图像数据时，判断所述宏块的编码类型是否为PCM类；若为非PCM类，则将所述图像数据按照无损压缩编码对应的解码方法进行解码；若确定所述宏块的编码类型为PCM类，则将所述图像数据按照Zip格式进行解码。
本示例所述的云桌面内容解码装置，客户端在接收到由云桌面的桌面图像划分的多个宏块压缩编码后的图像数据，以及所述图像数据对应的宏块类型后，对文字类或图形类的宏块压缩编码后的图像数据按照无损压缩编码对应的解码方法进行解码，对图像类的宏块压缩编码后的图像数据按照有损压缩编码对应的解码方法进行解码。本公开在对云桌面的桌面图像进行解码的过程中，由于对文字类或图形类的宏块采用的是无损压缩编码对应的解码方式，故客户端将所述文字类或图形类的宏块对应的图像数据进行解码后，可完全恢复原始数据而不引起任何失真，解决了云桌面的桌面图像发送到客户端后文字或图形显示模糊的问题，提升了用户使用体验。
参照图12，图12是本公开云桌面内容解码装置第二示例的模块示意图。基于上述图11所述的示例，本示例中，所述云桌面内容解码装置还包括：
第二去块效应滤波模块230，配置为在对接收到的图像数据进行解码后，检测解码后产生的宏块所构成的桌面图像是否为参考图像，若是，则对产生的所述宏块进行去块效应滤波。
其中，在对接收到的图像数据进行解码后，若检测到解码后产生的宏块所构成的桌面图像为参考图像时，则仅对图像类的宏块对应的图像数据解码后产生的宏块进行去块效应滤波。其中，对所述宏块进行竖直方向的去块效 应滤波处理时，若当前宏块的左边相邻的宏块属于文字类或图形类的宏块，则不对当前宏块与所述文字类或图形类的宏块相邻的竖直边界进行去块效应滤波。对图像类的宏块对应的图像数据解码后产生的宏块进行水平方向的去块效应滤波处理时，若当前宏块的上边相邻的宏块属于文字类或图形类的宏块，则不对当前宏块与所述文字类或图形类的宏块相邻的水平边界进行去块效应滤波。
本示例所述的云桌面内容解码装置，在对接收到的由多个宏块压缩编码后的图像数据进行解码后，若检测到所述图像数据解码后产生的宏块所构成的桌面图像为参考图像，对图像类的宏块对应的图像数据解码后所产生的宏块进行去块效应滤波处理，有效避免了在对所述图像数据进行解码之后，得到的桌面图像出现方块效应的问题。
参照图13，图13是本公开云桌面内容编码与解码系统的结构示意图。本示例中，所述云桌面内容编码与解码系统包括云桌面内容编码装置与云桌面内容解码装置，其中，所述云桌面内容编码装置为上述云桌面内容编码装置100；所述云桌面内容解码装置为上述云桌面内容解码装置200，在此不再赘述。
本公开实施例还提供了一种计算机可读存储介质，存储有计算机可执行指令，所述计算机可执行指令被处理器执行时实现以上描述的任一方法。
在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者装置不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者装置所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者装置中还存在另外的相同要素。
本领域普通技术人员可以理解，上文中所公开方法中的全部或某些步骤、系统、装置中的功能模块/单元可以被实施为软件、固件、硬件及其适当的组合。在硬件实施方式中，在以上描述中提及的功能模块/单元之间的划分不一定对应于物理组件的划分；例如，一个物理组件可以具有多个功能，或者一个功能或步骤可以由若干物理组件合作执行。某些组件或所有组件可 以被实施为由处理器，如数字信号处理器或微处理器执行的软件，或者被实施为硬件，或者被实施为集成电路，如专用集成电路。这样的软件可以分布在计算机可读介质上，计算机可读介质可以包括计算机存储介质(或非暂时性介质)和通信介质(或暂时性介质)。如本领域普通技术人员公知的，术语计算机存储介质包括在用于存储信息(诸如计算机可读指令、数据结构、程序模块或其他数据)的任何方法或技术中实施的易失性和非易失性、可移除和不可移除介质。计算机存储介质包括但不限于RAM、ROM、EEPROM、闪存或其他存储器技术、CD-ROM、数字多功能盘(DVD)或其他光盘存储、磁盒、磁带、磁盘存储或其他磁存储装置、或者可以用于存储期望的信息并且可以被计算机访问的任何其他的介质。此外，本领域普通技术人员公知的是，通信介质通常包含计算机可读指令、数据结构、程序模块或者诸如载波或其他传输机制之类的调制数据信号中的其他数据，并且可包括任何信息递送介质。
以上仅为本公开的示例性实施例，并非因此限制本公开的专利范围，凡是利用本公开说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本公开的专利保护范围内。
工业实用性
本公开所提出的云桌面内容编码与解码方法及装置、系统，通过将云桌面的桌面图像划分为多个宏块，然后对文字类或图形类的宏块进行无损压缩编码，对图像类的宏块进行有损压缩编码，并将编码后的图像数据及所述图像数据对应的宏块类型发送至客户端；另外，客户端在接收到所述图像数据及所述图像数据对应的宏块类型后，对文字类或图形类的宏块压缩编码后的图像数据按照无损压缩编码对应的解码方法进行解码，对图像类的宏块压缩编码后的图像数据按照有损压缩编码对应的解码方法进行解码。本公开在对云桌面的桌面图像进行编码与解码的过程中，由于对文字类或图形类的宏块采用的是无损压缩编码及无损压缩编码对应的解码方式，故客户端将所述文字类或图形类的宏块对应的图像数据进行解码后，可完全恢复原始数据而不引起任何失真，解决了云桌面的桌面图像发送到客户端后文字或图形显示模 糊的问题，提升了用户使用体验。因此本公开具有工业实用性。

Claims (19)

  1. 一种云桌面内容编码方法，包括：
    将云桌面的桌面图像按照预设的方式划分为多个宏块，并确定所述多个宏块分别对应的宏块类型(S110)；
    在所述宏块类型为第一类型的情况下，对所述宏块进行无损压缩编码，在所述宏块类型为第二类型的情况下，对所述宏块进行有损压缩编码，其中，所述第一类型包括文字类或图形类，所述第二类型包括图像类(S120)；
    将编码得到的图像数据及所述图像数据对应的宏块类型发送至客户端(S130)。
  2. 根据权利要求1所述的云桌面内容编码方法，其中，对所述宏块进行无损压缩编码的步骤包括：
    对所述宏块进行帧内预测和帧间预测得到预测残差(S121)；
    将所述预测残差按照预设的压缩方法进行变换，并将变换后的预测残差重新排序后进行基于上下文的自适应二进制算术CABAC熵编码(S122)。
  3. 根据权利要求2所述的云桌面内容编码方法，其中，对所述宏块进行无损压缩编码之后还包括：
    若编码得到的图像数据的比特数大于预设的阈值，则丢弃得到的图像数据，并将所述宏块的编码类型设置为脉冲编码调制PCM类；
    若编码得到的图像数据的比特数小于或等于预设的阈值，则保留得到的图像数据。
  4. 根据权利要求3所述的云桌面内容编码方法，其中，在将所述宏块的编码类型设置为PCM类之后还包括：
    将所述PCM类的宏块按照Zip格式进行压缩编码，得到对应的图像数据。
  5. 根据权利要求1所述的云桌面内容编码方法，其中，对所述宏块进行有损压缩编码的步骤包括：
    对所述宏块进行帧内预测和帧间预测得到预测残差(S123)；
    将所述预测残差进行整数离散余弦变换DCT，得到频域残差数据，并将所述频域残差数据量化后进行所述CABAC熵编码(S124)。
  6. 根据权利要求1至5任意一项所述的云桌面内容编码方法，其中，将编码得到的图像数据及所述图像数据对应的宏块类型发送至客户端(S130)之前还包括：
    检测所述桌面图像是否为参考图像，若是，则对有损压缩编码所得到的图像数据进行去块效应滤波(S140)。
  7. 一种云桌面内容解码方法，包括：
    接收由云桌面的桌面图像划分的多个宏块压缩编码后的图像数据，以及所述图像数据对应的宏块类型(S210)；
    在所述图像数据对应的宏块类型为第一类型的情况下，对所述图像数据按照无损压缩编码对应的解码方式进行解码，在所述图像数据对应的宏块类型为第二类型的情况下，对所述图像数据按照有损压缩编码对应的解码方式进行解码，其中，所述第一类型包括文字类或图形类，所述第二类型包括图像类(S220)。
  8. 根据权利要求7所述的云桌面内容解码方法，其中，所述接收由云桌面的桌面图像划分的多个宏块压缩编码后的图像数据，以及所述图像数据对应的宏块类型(S210)之后还包括：
    在所述图像数据对应的宏块类型为PCM类的情况下，对所述图像数据按照Zip格式进行解码。
  9. 根据权利要求7或8任意一项所述的云桌面内容解码方法，其中，所述云桌面内容解码方法还包括：
    在对接收到的图像数据进行解码后，检测解码后产生的宏块所构成的桌面图像是否为参考图像，若是，则对产生的所述宏块进行去块效应滤波(S230)。
  10. 一种云桌面内容编码装置(100)，包括：
    分类模块(110)，配置为将云桌面的桌面图像按照预设的方式划分为多个宏块，并确定所述多个宏块分别对应的宏块类型；
    编码模块(120)，配置为在所述宏块类型为第一类型的情况下，对所述宏块进行无损压缩编码，在所述宏块类型为第二类型的情况下，对所述宏块进行有损压缩编码，其中，所述第一类型包括文字类或图形类，所述第二类型包括图像类；
    传输模块(130)，配置为将编码得到的图像数据及所述图像数据对应的宏块类型发送至客户端。
  11. 根据权利要求10所述的云桌面内容编码装置(100)，其中，所述编码模块(120)包括：
    无损压缩编码单元(121)，配置为对所述宏块进行帧内预测和帧间预测得到预测残差；以及将所述预测残差按照预设的压缩方法进行变换，并将变换后的预测残差重新排序后进行基于上下文的自适应二进制算术CABAC熵编码。
  12. 根据权利要求11所述的云桌面内容编码装置(100)，其中，所述无损压缩编码单元(121)还配置为：
    若编码得到的图像数据的比特数大于预设的阈值，则丢弃得到的图像数据，并将所述宏块的编码类型设置为脉冲编码调制PCM类；
    若编码得到的图像数据的比特数小于或等于预设的阈值，则保留得到的图像数据。
  13. 根据权利要求12所述的云桌面内容编码装置(100)，其中，所述编码模块(120)还包括：
    Zip压缩编码单元(123)，配置为将所述PCM类的宏块按照Zip格式进行压缩编码，得到对应的图像数据。
  14. 根据权利要求10所述的云桌面内容编码装置(100)，其中，所述编码模块(120)还包括：
    有损压缩编码单元(122)，配置为对所述宏块进行帧内预测和帧间预测得到预测残差；以及将所述预测残差进行整数DCT变换，得到频域残差数据，并将所述频域残差数据量化后进行所述CABAC熵编码。
  15. 根据权利要求10至14任意一项所述的云桌面内容编码装置(100)， 其中，所述云桌面内容编码装置(100)还包括：
    第一去块效应滤波模块(140)，配置为将编码得到的图像数据及所述图像数据对应的宏块类型发送至客户端之前，检测所述桌面图像是否为参考图像，若是，则对有损压缩编码所得到的图像数据进行去块效应滤波。
  16. 一种云桌面内容解码装置(200)，包括：
    接收模块(210)，配置为接收由云桌面的桌面图像划分的多个宏块压缩编码后的图像数据，以及所述图像数据对应的宏块类型；
    解码模块(220)，配置为在所述图像数据对应的宏块类型为第一类型的情况下，对所述图像数据按照无损压缩编码对应的解码方式进行解码，在所述图像数据对应的宏块类型为第二类型的情况下，对所述图像数据按照有损压缩编码对应的解码方式进行解码，其中，所述第一类型包括文字类或图形类，所述第二类型包括图像类。
  17. 根据权利要求16所述的云桌面内容解码装置(200)，其中，所述解码模块(210)还配置为：
    在所述图像数据对应的宏块类型为PCM类的情况下，对所述图像数据按照Zip格式进行解码。
  18. 根据权利要求16或17任意一项所述的云桌面内容解码装置(200)，其中，所述云桌面内容解码装置(200)还包括：
    第二去块效应滤波模块(230)，配置为在对接收到的图像数据进行解码后，检测解码后产生的宏块所构成的桌面图像是否为参考图像，若是，则对产生的所述宏块进行去块效应滤波。
  19. 一种云桌面内容编码与解码系统，包括云桌面内容编码装置与云桌面内容解码装置，其中，所述云桌面内容编码装置为如权利要求10至15任意一项所述的云桌面内容编码装置(100)，所述云桌面内容解码装置为如权利要求16至18任意一项所述的云桌面内容解码装置(200)。
PCT/CN2017/113626 2016-12-08 2017-11-29 云桌面内容编码与解码方法及装置、系统 WO2018103568A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201611125592.5A CN108184118A (zh) 2016-12-08 2016-12-08 云桌面内容编码与解码方法及装置、系统
CN201611125592.5 2016-12-08

Publications (1)

Publication Number Publication Date
WO2018103568A1 true WO2018103568A1 (zh) 2018-06-14

Family

ID=62490690

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2017/113626 WO2018103568A1 (zh) 2016-12-08 2017-11-29 云桌面内容编码与解码方法及装置、系统

Country Status (2)

Country Link
CN (1) CN108184118A (zh)
WO (1) WO2018103568A1 (zh)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110913017A (zh) * 2019-12-19 2020-03-24 深圳市原创科技有限公司 一种基于云桌面的文件压缩传输方法
WO2020249100A1 (zh) * 2019-06-14 2020-12-17 华为技术有限公司 视频处理方法、装置及设备
CN113157226A (zh) * 2020-01-22 2021-07-23 阿里巴巴集团控股有限公司 远程数据的显示方法、装置、设备及机器可读存储介质
US20220182242A1 (en) * 2020-12-03 2022-06-09 International Business Machines Corporation Implementing opportunistic authentication of encrypted data
CN116708800A (zh) * 2023-08-01 2023-09-05 天津卓朗昆仑云软件技术有限公司 图像编码与解码方法、装置及系统

Families Citing this family (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109168006A (zh) * 2018-09-05 2019-01-08 高新兴科技集团股份有限公司 一种图形和图像共存的视频编解码方法
CN109831668B (zh) * 2018-12-29 2022-06-24 中兴通讯股份有限公司 数据压缩方法及装置、数据编码/解码方法及装置
CN112839226B (zh) * 2019-11-25 2024-02-23 深信服科技股份有限公司 一种图像编码、解码方法、相关装置及存储介质
CN111447452A (zh) * 2020-03-30 2020-07-24 西安万像电子科技有限公司 数据编码方法及系统
CN111506374A (zh) * 2020-04-12 2020-08-07 北京华如科技股份有限公司 基于不同对象的云应用ui交互方法及装置
CN111464812B (zh) * 2020-04-17 2022-06-10 重庆京像微电子有限公司 编码解码的方法、系统、装置、存储介质及处理器
CN111526364A (zh) * 2020-05-09 2020-08-11 西安万像电子科技有限公司 图像处理的方法、装置和系统
CN113573059B (zh) * 2021-09-23 2022-03-01 中兴通讯股份有限公司 图像显示方法、装置、存储介质及电子装置
CN115908165A (zh) * 2022-11-02 2023-04-04 阿里巴巴（中国）有限公司 云桌面图像的处理方法和图像处理方法、设备及存储介质
CN116781906A (zh) * 2023-07-03 2023-09-19 深圳市青葡萄科技有限公司 一种图文清晰度优化方法、设备及存储介质

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101217668A (zh) * 2008-01-14 2008-07-09 浙江大学 基于块分类的混合图像压缩方法
US8189661B2 (en) * 2009-03-09 2012-05-29 Microsoft Corporation Remote desktop protocol (RDP) tile image classification for improving compression efficiency and quality
CN103402091A (zh) * 2013-07-31 2013-11-20 上海通途半导体科技有限公司 云桌面图像分类与编码方法
CN103412701A (zh) * 2012-11-01 2013-11-27 华为技术有限公司 远程桌面图像处理方法及装置
CN103747077A (zh) * 2013-12-31 2014-04-23 华为技术有限公司 一种传输机制调整方法和服务端、客户端
WO2014079036A1 (zh) * 2012-11-23 2014-05-30 华为技术有限公司 图像压缩方法及图像处理装置
US8873872B1 (en) * 2012-04-18 2014-10-28 Matrox Graphics Inc. Division of entropy coding in codecs
US9025898B2 (en) * 2009-04-06 2015-05-05 Red Hat Israel, Ltd. Dynamically selecting compression method for graphics remoting
CN105681791A (zh) * 2016-01-18 2016-06-15 武汉理工大学 自适应远程桌面传输方法与系统

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP4584871B2 (ja) * 2006-06-09 2010-11-24 パナソニック株式会社 画像符号化記録装置および画像符号化記録方法
JP5278827B2 (ja) * 2007-09-06 2013-09-04 日本電気株式会社 映像符号化装置、映像復号装置、映像符号化方法、映像復号方法、映像符号化あるいは復号プログラム

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101217668A (zh) * 2008-01-14 2008-07-09 浙江大学 基于块分类的混合图像压缩方法
US8189661B2 (en) * 2009-03-09 2012-05-29 Microsoft Corporation Remote desktop protocol (RDP) tile image classification for improving compression efficiency and quality
US9025898B2 (en) * 2009-04-06 2015-05-05 Red Hat Israel, Ltd. Dynamically selecting compression method for graphics remoting
US8873872B1 (en) * 2012-04-18 2014-10-28 Matrox Graphics Inc. Division of entropy coding in codecs
CN103412701A (zh) * 2012-11-01 2013-11-27 华为技术有限公司 远程桌面图像处理方法及装置
WO2014079036A1 (zh) * 2012-11-23 2014-05-30 华为技术有限公司 图像压缩方法及图像处理装置
CN103402091A (zh) * 2013-07-31 2013-11-20 上海通途半导体科技有限公司 云桌面图像分类与编码方法
CN103747077A (zh) * 2013-12-31 2014-04-23 华为技术有限公司 一种传输机制调整方法和服务端、客户端
CN105681791A (zh) * 2016-01-18 2016-06-15 武汉理工大学 自适应远程桌面传输方法与系统

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2020249100A1 (zh) * 2019-06-14 2020-12-17 华为技术有限公司 视频处理方法、装置及设备
CN110913017A (zh) * 2019-12-19 2020-03-24 深圳市原创科技有限公司 一种基于云桌面的文件压缩传输方法
CN113157226A (zh) * 2020-01-22 2021-07-23 阿里巴巴集团控股有限公司 远程数据的显示方法、装置、设备及机器可读存储介质
US20220182242A1 (en) * 2020-12-03 2022-06-09 International Business Machines Corporation Implementing opportunistic authentication of encrypted data
US11917072B2 (en) * 2020-12-03 2024-02-27 International Business Machines Corporation Implementing opportunistic authentication of encrypted data
CN116708800A (zh) * 2023-08-01 2023-09-05 天津卓朗昆仑云软件技术有限公司 图像编码与解码方法、装置及系统

Also Published As

Publication number Publication date
CN108184118A (zh) 2018-06-19

Similar Documents

Publication Publication Date Title
WO2018103568A1 (zh) 云桌面内容编码与解码方法及装置、系统
US11936884B2 (en) Coded-block-flag coding and derivation
US11750841B2 (en) Methods and apparatuses for coding transform blocks
TWI670971B (zh) 基於調色板之視訊寫碼中之逃脫樣本寫碼
TWI693822B (zh) 基於調色板之視訊寫碼中之最大調色板參數
JP6144333B2 (ja) レートひずみ最適量子化におけるレベル決定
TWI666920B (zh) 用於視訊寫碼之具有執行長度碼之調色盤預測器信令
JP5718363B2 (ja) 大きいサイズの変換単位を用いた映像符号化、復号化方法及び装置
US8531321B1 (en) Signal processing and inheritance in a tiered signal quality hierarchy
US11765390B2 (en) Non-transform coding
JP2015508617A (ja) 映像の符号化における係数の符号化
US20140211858A1 (en) Spatially adaptive video coding
TW201547256A (zh) 在一視訊寫碼處理中之係數階寫碼
CN110149515B (zh) 数据传输方法及装置
CN103843338A (zh) 帧内编码图像块的自适应量化
US9008448B2 (en) Method and apparatus for successively encoding/decoding image
EP2680580A9 (en) Reduced worst-case context-coded bins in video compression with parity hiding
KR102020953B1 (ko) 카메라 영상의 복호화 정보 기반 영상 재 부호화 방법 및 이를 이용한 영상 재부호화 시스템
EP4354861A1 (en) Video decoding and coding method, device and storage medium
KR20200107745A (ko) 임베디드 이미지 코덱을 위한 서브-블록 기반 엔트로피 코딩
WO2023004590A1 (zh) 一种视频解码、编码方法及设备、存储介质
KR101659377B1 (ko) 인코딩 방법 및 그 시스템

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 17878315

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 17878315

Country of ref document: EP

Kind code of ref document: A1