WO1998035500A1 - Procede et dispositif pour optimiser les valeurs de quantification dans un codeur d'images - Google Patents

Procede et dispositif pour optimiser les valeurs de quantification dans un codeur d'images Download PDF

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WO1998035500A1
WO1998035500A1 PCT/US1998/001827 US9801827W WO9835500A1 WO 1998035500 A1 WO1998035500 A1 WO 1998035500A1 US 9801827 W US9801827 W US 9801827W WO 9835500 A1 WO9835500 A1 WO 9835500A1
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blocks
values
frame
quantization values
bits
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PCT/US1998/001827
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English (en)
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Jordi Ribas-Corbera
Shaw-Min Lei
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Sharp Kabushiki Kaisha
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Priority claimed from US09/008,137 external-priority patent/US6111991A/en
Application filed by Sharp Kabushiki Kaisha filed Critical Sharp Kabushiki Kaisha
Priority to JP53480498A priority Critical patent/JP2001526850A/ja
Publication of WO1998035500A1 publication Critical patent/WO1998035500A1/fr

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    • 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
    • H04N19/189—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the adaptation method, adaptation tool or adaptation type used for the adaptive coding
    • H04N19/19—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the adaptation method, adaptation tool or adaptation type used for the adaptive coding using optimisation based on Lagrange multipliers
    • 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
    • H04N19/124—Quantisation
    • H04N19/126—Details of normalisation or weighting functions, e.g. normalisation matrices or variable uniform quantisers
    • 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
    • H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
    • H04N19/146—Data rate or code amount at the encoder output
    • H04N19/147—Data rate or code amount at the encoder output according to rate distortion criteria
    • 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
    • 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
    • H04N19/169—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
    • H04N19/186—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 a colour or a chrominance component
    • 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
    • H04N19/60—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding
    • H04N19/61—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding in combination with predictive coding
    • 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
    • H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
    • H04N19/146—Data rate or code amount at the encoder output
    • 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
    • H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
    • H04N19/146—Data rate or code amount at the encoder output
    • H04N19/149—Data rate or code amount at the encoder output by estimating the code amount by means of a model, e.g. mathematical model or statistical model
    • 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
    • H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
    • H04N19/146—Data rate or code amount at the encoder output
    • H04N19/15—Data rate or code amount at the encoder output by monitoring actual compressed data size at the memory before deciding storage at the transmission buffer
    • 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
    • H04N19/30—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using hierarchical techniques, e.g. scalability

Definitions

  • the invention relates to computing quantization values used for encoding coeffients of a digital image or video frame and more particularly to optimizing the computed quantization values to reduce distortion in the digital image or video frame when encoding is performed with a limited number of bits.
  • the quality of encoded images is controlled by selecting one or more quality parameters.
  • Block-based image and video coders use a parameter known as a quantization scale or step for each block of pixels in the image.
  • the quantization steps are used for scaling pixel values within the same step ranges to the same values.
  • Image blocks encoded with the same quantization scale have approximately the same quality.
  • the number of bits needed for encoding an image depends on desired image quality (quantization scales) and on the inherent statistics of the image. As a result, different images encoded with the same scales (same image quality) will occupy a different number of bits.
  • the number of bits available for encoding one or several frames is fixed in advance, and some technique is necessary to select the quantization scales that will produce that target number of bits and encode the video frames with the highest possible quality. For example, in a digital video recording, a group of frames (GOP) must occupy the same number of bits for an efficient fast-forward/fast-rewind capability.
  • the channel rate, communication delay, and size of encoder buffers determine the available number of bits for one or more frames.
  • a first type of quantizer control method encodes each image block several times with a set of quantization scales. The number of bits produced for each case is measured and a scale for each block is smartly selected so the sum of the bits for all combined blocks hits the desired target bit number.
  • the first type of quantizer control techniques cannot be used for real-time encoding because of the high computational complexity required to encode each image block multiple times.
  • a second type of quantizer control technique measures the number of bits spent in previously encoded image blocks and measures other parameters such as, buffer fullness, block activity, etc. These measurements are used to select the quantization scale for the current block.
  • the second type of quantizer control is popular for real-time encoding because of its low computational complexity. However, the second type of quantizer control is inaccurate in achieving the target number of bits and must be combined with additional encoding techniques to avoid bit or buffer overflow and underflow.
  • a third type of quantizer control technique uses a model to predict the number of bits needed for encoding the image blocks.
  • the quantizer model includes the blocks' quantization scales and other parameters, such as, block variances.
  • the quantization scales are determined by some mathematical optimization of the encoder model.
  • the third type of quantizer control is computationally simple and can be used in real-time, but is highly sensitive to model errors and often produces inaccurate results.
  • a quantizer controller generates quantization values using a new block- adaptive, Lagrangian optimization.
  • the quantizer controller is updated and improved using information from earlier quantized blocks.
  • the quantizer controller is robust to model errors and produces results as accurate as type-1 quantizer control techniques, while having the simpler computational complexity of the type-2 quantizer control techniques.
  • the quantizer controller identifies a target bit value equal to a total number of bits available for encoding the frame.
  • a total amount of distortion in the frame is modeled according to the predicted quantization values assigned to each one of the blocks.
  • the predicted quantization values are characterized according to an amount of energy in each block and a number of bits available for encoding each block.
  • Optimum quantization values are adapted to each block by minimizing the modeled distortion in the frame subject to the constraint that the total number of bits for encoding the frame is equal to the target bit value.
  • Each block is then encoded with the optimized quantization value.
  • the quantizer controller is adaptive to each block by reducing quantization values for the blocks having less energy and increasing the quantization values for the blocks having more energy.
  • the quantization values assigned to the blocks are also optimized according to a number of image blocks remaining to be encoded and a number of bits still available for encoding the remaining image blocks.
  • Different weighting factors are optionally applied to the quantization values that vary the accuracy of the encoded blocks.
  • One weighting factor is applied to the quantization values according to the location of the block in the frame.
  • Optimized quantization values are applied to blocks in each frame, frames in a group of multiple frames or applied generally for any region in an array of image data.
  • the quantizer controller only encodes the image once to accurately generate the quantization values for each block.
  • the quantization values produce a target number of bits for the encoded image or video frame.
  • the quantizer controller is less computationally exhaustive than a quantizer control technique of similar accuracy.
  • the general framework of the quantizer controller can be used in a variety of quantizer/rate control strategies.
  • the quantizer controller can be used to select in real-time the value of the quantization scales for the Discrete
  • FIG. 1 is a schematic diagram of multiple image frames each including multiple blocks assigned optimized quantization values according to the invention.
  • FIG. 2 is a block diagram of an image coder according to one embodiment of the invention.
  • FIG. 3 is a step diagram for generating the optimized quantization values.
  • FIGS. 4 and 5 show results from applying the optimized quantization values to image data.
  • FIG. 6 is a block diagram of the quantizer controller according to one embodiment of the invention.
  • a block-based image coder 12 is used to describe the invention. However, the invention can be used for controlling the quantizer of any image or video coder.
  • images 15 are transmitted in multiple frames 26.
  • Each frame 26 is decomposed into multiple image blocks 14 of the same size, typically of 16x16 pixels per block.
  • the number of bits B ; produced after encoding an ith image block 14, is a function of the value of a quantization parameter Q; and the statistics of the block.
  • the pixel values for each image block 14 are transformed into a set of coefficients, for example using a Discrete Cosine Transform (DCT) in block transform 16. These coefficients are quantized in block quantization 18 and encoded in coder 20.
  • Bits B, of the encoded and quantized image blocks 14 are then transmitted over a communication channel 21 over a telephone line, microwave channel, etc. to a receiver (not shown).
  • the receiver includes a decoder that decodes the quantized bits and an inverse transform block that performs an Inverse Discrete Cosine Transform (IDCT).
  • IDCT Inverse Discrete Cosine Transform
  • Quantization of the transformed coefficients in quantization block 18 is a key procedure since it determines the quality with which the image block 14 will be encoded.
  • the quantization of the ith block 14 is controlled by the parameter, Q j .
  • Q f is known as the quantization step for the ith block and its value corresponds to half the step size used for quantizing the transformed coefficients.
  • Q ( is called the quantization scale and the jth coefficient of a block is quantized using a quantizer of step size QjW, , where w, is the jth value of a quantization matrix chosen by the designer of the MPEG codec.
  • N the number of 16x16 image blocks in one image frame 26.
  • the total number of bits B available for encoding one image frame 26 is:
  • the invention comprises a quantizer controller 22 (FIG. 1) that chooses optimum values for the Q s for a limited total number of available bits B for encoding the frames 26.
  • the quantizer controller 22 is implemented in a variety of different maps including in software in a programmable processing unit with dedicated hardware.
  • the image blocks 14 are said to be intracoded or of class intra.
  • many of the blocks 14 in a frame 26 are very similar to blocks in previous frames.
  • the values of the pixels in a block 14 are often predicted from previously encoded blocks and only the difference or prediction error is encoded.
  • These blocks are said to be intercoded or of class inter.
  • the invention can be used in frames with both intra and inter blocks.
  • the value Q is the quantizer step size or quantization scale
  • K and C are constants
  • ⁇ ( . is the empirical standard deviation of the pixels in the block
  • the value P-(j) is the jth pixel in the ith block and P. is the average of the pixel values in the block where,
  • the P-(j) 's are the values of the luminance and chrominance components of the respective pixels.
  • the model in equation 2 is derived using a rate-distortion analysis of the block's encoder.
  • the constant C in equation 2 models the average number of bits per pixel used for encoding the coder's overhead.
  • C accounts for header and syntax information, pixel color or chrominance components, transmitted Q values, motion vectors, etc. sent to the receiver for decoding the image blocks. If the values of K and C are not known, they are estimated with an inventive technique described below in the section entitled, "Updating the Parameters of the Encoder Model".
  • Equation 5 models distortion D for the N encoded blocks
  • the quantizer controller 22 selects the optimal quantization values, Q*,, Q* 2 , ... , Q* N , that minimize the distortion model in equation 5, subject to the constraint that the total number of bits must be equal to B as defined in equation 1, which can be expressed mathematically as follows:
  • the next objective is to find a formula for each of the Q * 's. To do this, the method
  • the optimal quantization parameter for the ith block is,
  • N, N-i+1 is the number of image blocks that remain to be encoded and B, is the number of bits available to encode them,
  • Equation 6 generate optimized quantization values that minimize distortion for a limited number of available bits.
  • the image in frame 26 in FIG. 1 will have less distortion than other quantization schemes when displayed on a display unit at the receiver end of the channel 21.
  • FIG. 3 describes the steps performed by quantizer controller 22 (FIG. 2) for selecting quantizer values used for encoding N image blocks 14 with B bits. Note that N could be the number of blocks in an image, part of an image, several images, or generally any region of an image.
  • Step 1. Receive energy values and initialization.
  • Pixel values for the N image blocks are obtained to the quantizer controller 22 from the digital image (FIG. 2) in step 1 A.
  • the amount of energy ⁇ ,. is derived from the DCT coefficients of the pixel values generated by transform block 16.
  • the values of the parameters K and C in the encoder model in equation 7 are known or estimated in advance.
  • Step 2 Compute the optimal quantization parameter for the ith block.
  • Step 4. Update quantizer values.
  • step 4 the parameters Kj +1 and C i+1 are updated in the quantizer controller 22.
  • Kj +] K
  • C i+1 C.
  • the updates K j+1 and C i+ are found using a model fitting technique.
  • a model fitting technique is described below in the section entitled "Updating
  • Step 5 Generate quantizer value for next block.
  • FIGS. 4 and 5 the frames of video sequences encoded by quantizer controller 22 where compared to those of a Telenor H.263 offline method, which is the quantizer control technique adopted for MPEG-4 anchors.
  • FIG. 4 the total number of bits per video frame obtained by the quantization technique described in FIG. 3 are shown in solid line.
  • the H.263 offline encoding technique is shown in dashed line. Encoding was performed on 133 frames of a well-known video sequence "Foreman".
  • the target number of bits B is 11200 bits per frame.
  • the quantizer controller 22 produces a significantly more accurate and steady number of bits per frame. Similar results were obtained for a wide range of bit rates. In the experiments, there were little if no visible differences in the quality of the two encoded video sequences. The signal to noise ratio performance of the images processed by quantizer controller 22 was only 0.1-0.3 dB lower on average. Thus, even though the image is only encoded once, quantizer controller 22 achieves the target bit rate accurately with high image quality at every frame.
  • Alternative Implementations Several quantization variations are based on the base quantization optimization framework discussed above. If the computation of all ⁇ A 's in Step IB of FIG. 3 cannot be performed in advance, a good estimate for S, is used, such as the value of S, from the previous video frame 26.
  • equation 9 A low-complexity estimate of S, can be used, in order to further reduce computational complexity.
  • equation 9 For the low complexity estimate, equation 3 is replaced by equation 9,
  • A is the number of pixels in the jth region.
  • the region of quantization does not need to be a block. Additional model parameters ⁇ and ⁇ can either be set prior to quantization or obtained using parameter estimation techniques described below. If the quantization model in equation 10 is used in step 2, the optimized quantization values Q * 's are derived using equation 11,
  • step 3 S 1+1 is replaced
  • performance of the quantizer controller 22 can be improved by dividing the standard deviation of the intra blocks by a factor f .
  • the factor -J$ is applied as follows:
  • the values K 7 and K P are the averages of the K's measured for the intra and inter blocks, respectively.
  • the same quantizer controller 22 shown in FIG.1 can be used for encoding one or several frames.
  • the parameters ⁇ , , ⁇ ; , B( , are the weight, variance, and bits for the ith frame, respectively.
  • the parameters K, and C, are updates of the coder model for that frame.
  • each image block 14 can be encoded several times and, using a classical model fitting procedure (e.g., least- squares fit, linear regression, etc.), a good estimate of the K,'s and C,'s for the blocks can be found in advance. Then, in step 2, the quantization values Q * are determined according to,
  • model parameters in one embodiment of the invention are updated or estimated on a block-by-block basis using the following weighted averages,
  • B DCT>1 is the number of bits spent for the DCT coefficients of the i-th image
  • the updates are a linearly weighted average of K j , C j and their respective initial estimates K hin C,
  • the values of the quantizer values used Q*,, ..., Q* N need to be encoded and sent to the decoder.
  • quantizer values are encoded in a raster-scan order and there is a five-bit penalty for changing the quantizer value.
  • bit overhead for changing the quantizer values is negligible and the optimization techniques described above are effective.
  • this overhead is significant and some technique is needed for constraining the number of times that the quantizer changes.
  • existing optimization methods that take quantization overhead into account are mathematically inaccurate or computationally expensive.
  • a heuristic method joins blocks of similar standard deviation together into a set so that the quantizer value remains constant within the set. This technique is referred to as block joining, and reduces the changes of the quantizer at lower bit rates. Block joining is accomplished by choosing the values of the ⁇ ,. weights as follows,
  • B/(AN) is the bit rate in bits per pixel for the current frame.
  • FIG. 6 is a detailed block diagram of the quantizer controller 22 shown in FIG. 2.
  • the quantizer controller 22 in one embodiment is implemented in a general purpose programmable processor.
  • the functional blocks in FIG. 6 represent the primary operations performed by the processor.
  • Initialization parameters in block 31 are either derived from pre-processing the current image or from parameters previously derived from previous frames or from prestored values in processor memory (not shown). Initialization parameters include N,, S-, B,, K, and C, (or K N+1 and C N+ , from a previous frame).
  • the image is decomposed into N image blocks 14 (FIG. 2) of A pixels in block 30.
  • the energy of the pixels in each block is computed in block 32.
  • the weight factors assigned to each block are computed in block 34.
  • the amount of energy left in the image is updated in block 40 and the bits left for encoding the image are updated in block 38.
  • Parameters for the encoder model are updated in block 42 and the number of blocks remaining to be encoded are tracked in block 44.
  • the processor in block 36 then computes the optimized quantizer step size according to the values derived in blocks 32, 34, 40, 38, 42 and 44.

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Abstract

L'invention concerne un contrôleur de quantification (22) qui identifie une valeur binaire cible équivalant au nombre total de bits disponibles pour le codage d'une trame. On modélise dans la trame une quantité totale de distorsion d'après certaines valeurs de quantification résultant de prévisions, pour chacun des différents blocs. Les valeurs de quantification qui résultent des prévisions sont caractérisées en fonction d'une quantité d'énergie dans chaque bloc et d'un certain nombre de bits disponibles pour le codage de chaque bloc. On optimise les valeurs de quantification correspondant aux différents blocs en réduisant au minimum la distorsion modélisée dans la trame à condition que le nombre total de bits pour le codage de la trame soit équivalent à la valeur binaire cible. On code ensuite chaque bloc avec la valeur de quantification optimisée.
PCT/US1998/001827 1997-02-11 1998-01-30 Procede et dispositif pour optimiser les valeurs de quantification dans un codeur d'images WO1998035500A1 (fr)

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EP1102492A2 (fr) * 1999-11-18 2001-05-23 Sony United Kingdom Limited Compression de données
WO2002080567A1 (fr) * 2001-03-30 2002-10-10 Sony Corporation Dispositif de quantification de signal d'image et son procede
WO2004010702A1 (fr) 2002-07-22 2004-01-29 Institute Of Computing Technology Chinese Academy Of Sciences Procede et dispositif de controle de debit binaire combines avec une optimisation debit-distorsion
WO2004091221A2 (fr) * 2003-04-04 2004-10-21 Avid Technology, Inc. Debit binaire fixe, compression et decompression intratrames d'une image video
US7403561B2 (en) 2003-04-04 2008-07-22 Avid Technology, Inc. Fixed bit rate, intraframe compression and decompression of video
WO2009157827A1 (fr) * 2008-06-25 2009-12-30 Telefonaktiebolaget L M Ericsson (Publ) Régulation de débit d'évaluation de ligne
US7916363B2 (en) 2003-04-04 2011-03-29 Avid Technology, Inc. Bitstream format for compressed image data
EP1892965A3 (fr) * 2003-04-04 2011-04-06 Avid Technology, Inc. Compression et décompression intratrames d'une image video ayant un débit binaire fixe
US9445102B2 (en) 2011-07-07 2016-09-13 Fraunhofer-Gesellschaft Zur Foerderung Der Angewandten Forschung E.V Model parameter estimation for a rate- or distortion-quantization model function

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