Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T1/00—General purpose image data processing
  • G06T1/0021—Image watermarking
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T1/00—General purpose image data processing
  • G06T1/0021—Image watermarking
  • G06T1/005—Robust watermarking, e.g. average attack or collusion attack resistant
• • H—ELECTRICITY
  • H03—ELECTRONIC CIRCUITRY
  • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
  • H03M13/00—Coding, decoding or code conversion, for error detection or error correction; Coding theory basic assumptions; Coding bounds; Error probability evaluation methods; Channel models; Simulation or testing of codes
  • H03M13/01—Coding theory basic assumptions; Coding bounds; Error probability evaluation methods; Channel models; Simulation or testing of codes
• • H—ELECTRICITY
  • H03—ELECTRONIC CIRCUITRY
  • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
  • H03M13/00—Coding, decoding or code conversion, for error detection or error correction; Coding theory basic assumptions; Coding bounds; Error probability evaluation methods; Channel models; Simulation or testing of codes
  • H03M13/25—Error detection or forward error correction by signal space coding, i.e. adding redundancy in the signal constellation, e.g. Trellis Coded Modulation [TCM]
• • H—ELECTRICITY
  • H03—ELECTRONIC CIRCUITRY
  • H03M—CODING; DECODING; CODE CONVERSION IN GENERAL
  • H03M7/00—Conversion of a code where information is represented by a given sequence or number of digits to a code where the same, similar or subset of information is represented by a different sequence or number of digits
  • H03M7/30—Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction
  • H03M7/3082—Vector coding
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2201/00—General purpose image data processing
  • G06T2201/005—Image watermarking
  • G06T2201/0051—Embedding of the watermark in the spatial domain

Definitions

• the communication channel is disturbed by a noise x modeled by a random variable according to a Normal distribution X ⁇ N (0, Q), and by a noise z modeled by a random variable following a Normal distribution Z ⁇ N (0, N) .
• Figure 1 is a simplified schematic view of a communication channel disturbed by two sources of noise
• t [n - 1] 0, 5 4 rto € (2 * PI + € t € n2 (x [n], x [n-1]), 2 * PI);
• Step 11 is a step of encoding the message rri j .
• the quantization step d of the code is calculated with the formula:
• the quantization step d is then used to determine, by quantization, code words U.
• the quantization coding is for example of the turbo type TCQ. Such a coding guarantees a good distribution of the code words with respect to each other.
• SCS-type quantization encoding could be used as described in the article by JJ Eggers, R. Bauml, R. Tzschoppe, B. Girod. Costa scalar scheme for information embedding (IEEE Transactions on Signal Processing, 2002) or quantization coding of the QIM type as described in the article by B. Chen , G. Wornell. "Index Quantization Quantization” (IEEE Trans. Theory, 2001).
• the (n-1) first components of the vector t are quantized to select the code word Uj i closest to the adjacent information, among the code words ⁇ associated with the message rri j .
• the closest code word in the Euclidean sense which corresponds to the formula below:
• M is a set of messages that can be transmitted.
• RRI is called j j th message of the set M j is an integer between 1 and the number of messages of all Mr Consider for illustration that all four million messages.
• the number q of code words U associated with each message rri j is equal to four.
• the code word u s u is the closest to the adjacent information t and is therefore selected.
• the decoding method is described.
• the method is for example executed by a decoding device 41 integrated in a reception equipment 40 (FIG. 10).
• step 20 the signal y, corresponding to the signal w, is received.
• the received signal is initially characterized by a Cartesian coordinate vector (y [l], .., y [n]).
• the vector is changed to coordinate system to obtain a vector r characterizing the signal received in the spherical coordinate system.
• the change of coordinate system is for example realized by the algorithm 1, replacing t [i] by r [i] and x [i] by y [i].
• step 21 the first n-1 components r [1] to r [nl] of the vector r are transmitted to the input of a decoder by quantization of the decoding device, in order to determine the code word U i i contained in the received signal.
• U code word determines the RRI j transmitted message.
• the key k is used to generate a pseudo-random noise, which is added to the first n-1 components of the vector t before the encoding of the message rri j .
• FIG. 9 represents a particular embodiment in which the encoding device 31 is integrated in the transmitting equipment 30.
• the transmitting equipment 30 also comprises transmission means 32.
• the reception means 42 are connected to the decoding device 41, for transmitting the received signal y to the decoding device 41.

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• Physics & Mathematics (AREA)
• Engineering & Computer Science (AREA)
• Theoretical Computer Science (AREA)
• Probability & Statistics with Applications (AREA)
• General Physics & Mathematics (AREA)
• Compression, Expansion, Code Conversion, And Decoders (AREA)

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• 2011
  • 2011-06-17 EP EP11737987.5A patent/EP2589154A1/de not_active Withdrawn
  • 2011-06-17 US US13/805,441 patent/US9082173B2/en not_active Expired - Fee Related
  • 2011-06-17 WO PCT/FR2011/051398 patent/WO2012001263A1/fr active Application Filing

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