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WO2000007375A1 - Systeme de codage video - Google Patents

Systeme de codage video Download PDF

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Publication number
WO2000007375A1
WO2000007375A1 PCT/EP1999/005488 EP9905488W WO0007375A1 WO 2000007375 A1 WO2000007375 A1 WO 2000007375A1 EP 9905488 W EP9905488 W EP 9905488W WO 0007375 A1 WO0007375 A1 WO 0007375A1
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WO
WIPO (PCT)
Prior art keywords
motion
frame
series
estimation system
subsampling
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Application number
PCT/EP1999/005488
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English (en)
Inventor
Marta Karczewicz
Levent Oktem
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Nokia Mobile Phones Limited
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.)
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Publication date
Application filed by Nokia Mobile Phones Limited filed Critical Nokia Mobile Phones Limited
Priority to AU52902/99A priority Critical patent/AU5290299A/en
Priority to EP99938387A priority patent/EP1101359A1/fr
Publication of WO2000007375A1 publication Critical patent/WO2000007375A1/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
    • H04N19/50—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
    • H04N19/537—Motion estimation other than block-based
    • H04N19/543—Motion estimation other than block-based using regions
    • 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
    • H04N19/53—Multi-resolution motion estimation; Hierarchical motion estimation
    • 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
    • H04N19/537—Motion estimation other than block-based
    • 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
    • H04N19/56—Motion estimation with initialisation of the vector search, e.g. estimating a good candidate to initiate a search

Definitions

  • the present invention relates to a video coding system.
  • it relates to a system for the compression of video sequences using motion compensated prediction.
  • Figure 1 illustrates an encoder having a motion estimation block and figure 2 illustrates a corresponding decoder.
  • Motion compensated prediction in such a system is outlined below.
  • Motion Compensated (MC) prediction is a widely recognized technique for compression of video. It utilizes the fact that in a typical video sequence, image intensity value in a particular frame can be predicted using image intensities of some other already coded and transmitted frame, given the motion trajectory between these two frames.
  • the operating principle of motion compensated video coders is to minimize the prediction error E n (x,y) , i.e., the difference between the frame being coded I Oh( ⁇ ,y) called the current frame and the prediction frame P n ( ⁇ ,y) ( Figure 1 ):
  • the prediction error E n ( ⁇ ,y) is compressed and the compression process typically introduces some loss of information.
  • the compressed prediction error denoted by E n (x,y) is sent to the decoder.
  • the prediction frame P vinegar( ⁇ ,y) is constructed by the motion compensated prediction block in Figure 1 and Figure 2.
  • the prediction frame is built using pixel values of the reference frame denoted by RRON( ⁇ ,y) and the motion vectors of pixels between the current frame and the reference frame using the formula
  • the reference frame is one of the previously coded and transmitted frames (e.g. a frame preceding the one being coded) which at a given instant is available in the Frame Memory of the encoder and of the decoder.
  • the pair of numbers is called the motion vector of the pixel in location (x,y) in the current frame.
  • ⁇ x( ⁇ ,y) and Ay( ⁇ ,y) are the values of horizontal and vertical displacements of this pixel, respectively.
  • Motion vectors are calculated by the motion estimation block in the encoder shown in Figure 1.
  • the set of motion vectors of all pixels of the current frame [ ⁇ x(-), ⁇ yQ] is called the motion vector field and is transmitted to the decoder.
  • pixels of the coded current frame I Oh( ⁇ ,y) are reconstructed by finding the prediction pixels in the reference frame R n (-) using the received motion vectors and by adding the received prediction error E n (x,y) , i.e.,
  • the transmission channel available for the compressed video bit stream is very narrow, it is possible to reject the effect of prediction errors. Then it is not necessary to compress and transmit the prediction error, and the spare bits from the transmission channel and spare calculation power can be used for other purposes, e.g., to improve the frame rate of the video signal.
  • the rejection of prediction errors leads to defective pixel elements in the visible video picture, but depending on the demands of the application in use it may be acceptable.
  • Block based coders where the current frame is divided into fixed and a priori known blocks, e.g., 16x16 pixels blocks in international standard
  • Segmentation based (region based) coders where the current frame is divided into arbitrarily shaped segments, e.g., obtained by a segmentation algorithm (Figure 3b).
  • Figure 3b For examples refer to Centre de Morphologie Mathematique (CMM), "Segmentation algorithm by multicriteria region merging," Document SIM(95)19, COST 211ter Project Meeting, May 1995 and P. Cicconi and H. Nicolas, "Efficient region-based motion estimation and symmetry oriented segmentation for image sequence coding," IEEE Transactions on Circuits and Systems for Video Technology, Vol. 4, No. 3, June 1994, pp. 357-364)
  • segments include at least a few tens of pixels.
  • motion vector field model Almost all of the motion vector field models commonly used are additive motion models.
  • Motion compensated video coding schemes may define the motion vectors of image segments by the following general formula:
  • Ax(x, y) a n + a,x + a 7 y (7)
  • Ax(x,y) a 0 + a l x + a 2 y + a 3 xy + a 4 x + a 5 y
  • Ay(x,y) b 0 + b l x + b 2 y + b 3 xy + b 4 x 2 + b 5 y 2 W
  • the affine motion model presents a very convenient trade-off between the number of motion coefficients and prediction performance. It is capable of representing some of the common real-life motion types such as translation, rotation, zoom and shear by only a few coefficients.
  • the quadratic motion model has a good prediction performance, but it is less popular in coding than the affine model, since it uses more motion coefficients, while the prediction performance is not much better. Furthermore, it is computationally more costly to estimate the quadratic motion than to estimate affine motion.
  • the Motion Estimation block calculates motion vectors [Ax(x,y),Ay(x,y)] of the pixels of a given segment S k which minimize some measure of prediction error in this segment.
  • a meaningful additive measure of prediction error has the form:
  • (9) is a highly non-linear function of many variables and there is thus no practical technique which is capable of always finding the absolute minimum of (9) in finite time. Accordingly, motion estimation techniques differ depending on the algorithm for minimization of the chosen measure of prediction error.
  • One technique is the full search.
  • the value of the cost function is calculated for all the possible combinations of allowed values of the motion coefficients (restricted by the range and precision with which motion coefficients can be represented).
  • the values of motion coefficients for which the cost function is minimized are chosen to represent the motion vector field.
  • the full search technique is usually used only to estimate motion coefficients of the translational motion model and cannot be straightforwardly generalized for other motion models, due to computational burden. In a straight-forward generalization, the computational complexity of the algorithm is exponentially increased by the number of motion coefficients used to represent the motion vector field.
  • the n th iteration step consists of: 1. computing the approximate quadratic function using first and second derivatives of the actual function using the motion coefficient resulting from (n-1 step, 2. computing the coefficient vector minimizing the approximate function, and assigning the result to the motion coefficient of n th step.
  • the main problem associated with the Gauss-Newton algorithm is that it converges only towards local minima, unless the initial motion coefficients lie in the attraction domain of the global minimum. Thus it is necessary to provide the Gauss-Newton algorithm with a sufficiently good initial guess of the actual optimum.
  • Two different techniques are usually used to improve the convergence of the Gauss-Newton algorithm: 1. motion estimation using multiresolution image pyramids,
  • the technique of motion estimation using multiresolution image pyramids is based on the assumption that low-pass filtering the current frame and the reference frame will erase the local minima and help the algorithm to converge to the global minimum.
  • Motion estimation is performed first on the low-pass filtered (smoothed) versions of the reference and current frames, and the result is fed as input to the motion estimation stages using less smoothed images.
  • the final estimation is performed on non-smoothed images.
  • Some variants of this class additionally down-sample the low-pass filtered images, to reduce the number of computations. (For examples of this technique, see H.
  • low-pass filtering of the images does not necessarily erase local minima. Furthermore, this may shift the location of global minimum.
  • Down-sampling can be applied only when the low-pass filtering is sufficient to prevent aliasing. Moreover, convergence becomes more difficult due to the reduction of number of pixels in the region.
  • a complex motion model can be approximated by a lower order motion model.
  • the system according to the present invention achieves statistically low prediction error with relatively little complexity by dynamically switching between statistically valid assumptions varying in strength.
  • a motion estimation system for a video coder comprising:means for receiving a video frame to be coded; means for smoothing the received frame; means for subsampling the smoothed frame and for forwarding the subsampled frame to a series of motion estimators; a series of motion estimators of varying complexity, for estimating a motion vector field between the said received frame and a reference frame; and control means for selecting the subsequent motion estimator in the series only if a prediction error associated with the motion vector field estimated by the currently selected motion estimator exceeds a predetermined threshold.
  • control means is arranged to activate the smoothing means in dependence on the state of the sub-sampling means.
  • control means is arranged to activate the smoothing means only if the sub-sampling means is also activated.
  • the smoothing means comprises a single low-pass filter and the sub-sampling means comprises a single sub-sampler. If the control means causes no smoothing to occur, the control means instructs the sub- sampling means not to sub-sample the image. If the control means sets the smoothing means to smooth the image (i.e. to pass it through the low-pass filter), the control means also instructs the sub-sampling means to sub-sample the image.
  • the smoothing means may take the form of a series of low pass filters.
  • the control means selects the level of smoothing during minimisation depending on the change in prediction error. For example, if the change is below a threshold for a certain level of smoothing, then at least the next level of smoothing may be jumped. Alternatively, if the change is at, or above, the threshold, then the next level of smoothing may be selected.
  • the series of motion estimators may comprise a series of motion models, a series of minimisers, or a combination of both.
  • it may comprise a hierarchy of motion models in order of complexity (e.g. zero motion model, translational motion model, affine motion model, and quadratic motion model).
  • it may comprise a series of minimisers, for example for one particular model (e.g. for a linear model, the series of minimisers could be Quasi-Newton and Gauss-Newton.
  • Another option is a combination of motion models and minimisers.
  • the predetermined threshold may differ, at different stages of minimisation and/or for different models.
  • the invention also relates to a video coder comprising a motion estimation system according to the present invention.
  • a motion estimation method for coding a video frame comprising:receiving a video frame to be coded; smoothing the received frame; subsampling the smoothed frame and for forwarding the subsampled frame to a series of motion estimators; estimating a motion vector field between the said received frame and a reference frame, using a motion estimator from a series of motion estimators of varying complexity; determining whether a prediction error associated with the motion vector field estimated by the currently selected motion estimator exceeds a predetermined threshold and, only if so, selecting the subsequent motion estimator in the series.
  • Figure 1 illustrates an encoder for the motion compensated coding of video
  • Figure 2 illustrates a decoder for the motion compensated coding of video
  • Figure 3(a) illustrates the division of the current frame for block based motion compensation
  • Figure 3(b) illustrates the division of the current frame for segmentation based motion compensation
  • Figure 4 illustrates a motion estimation system according to an embodiment of the invention
  • Figure 5 illustrates the low pass filtering block shown in Figure 4
  • Figure 6 illustrates the sub-sampling block shown in Figure 4
  • Figure 7 is a block diagram of the motion model selector shown in Figure 4
  • Figure 8 is a block diagram of the cost function minimiser shown in
  • Figure 9 is a flow diagram of a preferred implementation of the result verification block shown in Figure 4.
  • Figure 10 is a flow diagram of a Gauss-Newton minimisation stage according to an embodiment of the present invention.
  • Figure 11 is a flow diagram of a Quasi-Newton minimisation stage for use in an embodiment of the present invention.
  • a motion estimation system of a preferred embodiment of the present invention is shown in Figure 4. It consists of five main building blocks, namely a low-pass filtering block, a sub-sampling block 20, a motion model selector block 30, a cost function minimiser block 40 and a result verification block 50. Each of these blocks is described below.
  • FIG. 5 is a block diagram of a preferred low-pass filtering block 10.
  • the inputs to this block are the reference frame 5, the current frame 6, and the information about the required level of filtering (smoothness switch update
  • the low-pass filtering block 10 consists of a bank 12 of low-pass filters and a multiplexer 14.
  • the filters in the bank must be designed in such a way that the cut-off frequencies of low pass filter 1 , low pass filter 2, ..., low pass filter n form a decreasing sequence.
  • the multiplexer is in the form of a smoothness selector 14.
  • the inputs to the smoothness selector 14 are reference frame 5 and current frame 6, and their low-pass filtered versions at various levels of smoothness.
  • the smoothness switch update 7 chooses which image pair will be the output.
  • Figure 6 shows the block diagram of the Subsampling block 20.
  • the inputs to this block are reference frame 5 (possibly smoothed), the current frame 6 (possibly smoothed), segmentation information, and the information about the required level of subsampling (subsampling switch update 26).
  • the Subsampling block consists of a bank of subsamplers 22 and a multiplexer 24.
  • Each subsampler in the bank (denoted 'Subsample by mxm') subsamples the input images by taking every other m'th pel in both horizontal and vertical directions, where m is an integer power of 2.
  • the multiplexer 24 is a Subsampling Selector.
  • the inputs to the Subsampling Selector are the reference and current frames (5,6) (possibly smoothed) and segmentation information; and their subsampled versions at various levels.
  • the Subsampling Switch update 26 chooses which image pair will be the output. Aliasing might occur if Subsampling Switch 26 is not consistent with Smoothness Switch 7. It is the responsibility of Result Verification block 50 to generate consistent switch signals 26 and 7.
  • Figure 7 shows a preferred motion model selector block 30.
  • the motion model selector block comprises a motion model multiplexer 32.
  • the input to this block is motion model switch signal 9.
  • Model selection block 32 is a multiplexer, which makes a selection among a bank 34 of motion models, via motion model switch signal 9.
  • the motion models in the bank 34 vary in order of complexity.
  • the order of complexity refers to the number of basis functions used in representing the motion.
  • there is a model 'no motion' 34a for which all the motion coefficients are set to zero.
  • Figure 8 shows the block diagram of a preferred cost function minimizer 40.
  • the cost function minimizer block 40 is the place where minimization of the cost function is actually performed.
  • the inputs to this block are the smoothed reference image 5 and current image 6, segmentation information from the subsampling block 20, selected motion model from the motion model selector 30, and current motion coefficients, current cost function value and minimization method switch signal 17 from the result verification block 50.
  • This cost function minimiser block 40 performs minimization by one of the methods in its bank 43. There are three methods in the bank, and the selection of the method is performed by minimization method switch signal 17. These three methods are segment matching 44, Gauss-Newton minimisation 46 and Quasi-Newton minimisation 48, and are further described below.
  • Segment matching can be selected only for the translational motion model.
  • the value of the cost function is calculated for the selected set of values of motion coefficients a 0 and b 0 .
  • the values of a 0 and b 0 which yield the smallest value of the prediction error in (6) are chosen as the solution.
  • the search is performed on a quantized grid, i.e. only the scalar quantized values of a 0 and b 0 are used.
  • the search is done by evaluating some or all of the candidate pairs on the grid, and choosing the one yielding the least cost function value.
  • the Gauss-Newton step can be used for any motion model except 'No
  • the Gauss-Newton method is a specific form of Newton's method, commonly used when the cost function to be minimized is a sum of squares. Newton's method is summarized below. Let e(a) be the cost function as a function of parameter vector a . Let a f be the current parameter vector, being input to the minimization iteration. Then, this cost function can be quadratically approximated around a f as follows :
  • Quasi-Newton minimization is a well known iterative procedure which continues iterations until a convergence to a minimum is achieved. (Again, see R. Fletcher, "Practical Methods of Optimization", Second Edition, John Wiley & Sons, 1987, Chapter 3 and Chapter 6).
  • the cost function minimiser 40 outputs a minimised cost function value 45 and a motion coefficient vector 46. Turning now to the result verification block 50 shown in Figure 4, this controls the other blocks in the system by generating switch signals.
  • switch signals are smoothness switch update 7 (determining the smoothness level to be used in the current iteration), motion model switch 9 (determining the motion model to be used in the current iteration), and minimization method switch 17 (determining the minimization method to be used in the current iteration).
  • Any combination of switch signals generated by this block has an underlying set of assumptions. The purpose of this block 50 is to find the strongest set of assumptions that is valid for the current state. Once the set of assumptions is determined, a new set of switch signals is generated, and new motion coefficients and a new cost function value result. By comparing these to the previous ones, the result verification block determines:
  • the result verification block 50 keeps the 'current motion coefficients', which are the motion coefficients yielding the smallest value of the cost function so far, and a 'current cost function value' which is the smallest value of the cost function so far. If the iterations are continued, current motion coefficients and current cost function value are fed to cost function minimizer 40.
  • the generation of the new switching signals depending on the comparison of results can be done in numerous ways. A preferred way will be described below.
  • a convergence flag signal 51 is set, current motion coefficients are output as 'final motion coefficients' 52, and current cost function value is output as 'final cost function value' 53.
  • a major advantage of this invention over previously known solutions is its ability to estimate the motion of a region that minimizes the prediction error, while keeping the computational complexity low.
  • the result verification block 50 is a powerful tool for keeping the computational complexity of the system at a minimum. By context-dependent switching between techniques, this block allows high performance motion estimation, involving computationally complex techniques only when necessary.
  • the system can find motion coefficients of a segment for:
  • the filter bank 12 includes only one low-pass filter 12a. This is a separable filter, with 10 taps in each direction.
  • the filter coefficients are as follows:
  • This filter is designed with subsampling in mind: thus, a low-pass filter which minimizes (in a least squares sense) the loss incorporated by the cascade operation of "subsampling by 2x2 and then upsampling by cubic convolution interpolation" is implemented.
  • the filter obtained this way is then truncated to provide 10 taps.
  • the purpose of this truncation is to reduce the computational complexity of the filtering operation.
  • Cubic convolution interpolation is preferred for upsampling since it is the chosen subpixel evaluation strategy. Further details can be found in R. G. Keys, "Cubic convolution interpolation for digital image processing," IEEE Trans.
  • Only one subsampler 22 is provided in the bank of the sub-sampling block 20. This sub-sampler causes the signal to be subsampled by 2x2.
  • the subsampling switch signal 26 either chooses this subsampler, or no subsampling.
  • smoothness switch 7 is set to 'None' by the result verification block 50 if and only if subsampling switch 26 is set to 'None'; and smoothness switch 7 is set to 'Smooth' if and only if subsampling switch 26 is set to 'Subsample by 2x2'.
  • motion model selector's bank 34 there are three motion models in the motion model selector's bank 34: "no motion" model 34a, translational model 34b, affine model 34c.
  • the segment matching performed is 'full-pel segment matching with full search'. This operation requires testing all integer-valued motion coefficient pairs in a certain range to find the pair minimizing the cost function.
  • the Gauss-Newton step is employed only when the motion model is affine.
  • c( ⁇ ) [dx, y, dx, x, dx, dy, y, dy, x, dy, ] ; dx, denoting the horizontal image derivative at i'th pixel, and dy, denoting the vertical image derivative at i'th pixel.
  • g is a vector of size 6 with the entries as follows:
  • Cubic convolution interpolation is used for subpixel evaluation.
  • Image derivatives are also computed using cubic convolution: the derivatives of the continuous function obtained by cubic convolution interpolation are computed, and they are interpreted as the image derivatives.
  • the Quasi-Newton minimisation 48 used in this preferred embodiment is known as 'Quasi-Newton minimization with BFGS update formula and inexact line minimization' (See R. Fletcher, "Practical Methods of
  • Figure 9 shows the flow diagram of the operation of the result verification block 50 of this preferred embodiment.
  • a first assumption (90) of "no motion” is set by switching the smoothness level to "None", the sub-sampling level to
  • the subsampling switch 26 to 'Subsampling by 2x2', the model is switched to Translational', and the method to 'Segment Matching' (92) (subsampling is performed to reduce the complexity of operation, and smoothing is performed to prevent aliasing that would occur otherwise in subsampling). If the cost function resulting from this step is below the cost function corresponding to zero-motion, the best-so-far coefficients are updated (93) by setting the values of a 0 and b 0 in (7) to the values computed by segment matching, and setting the other coefficients to zero.
  • the motion model is switched (105) to 'Affine', the smoothness switch 7 to 'None', the subsampling switch 26 to 'None', and the minimization method to 'Gauss-Newton step'.
  • iterations (106-109) are performed until one of the following occurs: • the drop in cost function in the last iteration is below a specified threshold TH3 (107),
  • TH4 a specified threshold
  • the value of the cost function is compared to the smallest value so far (113). If a decrease in cost function value is obtained, the motion coefficients and the value of the cost function are saved.
  • the Result Verification block 50 terminates the search (99), assigning the smallest cost function value obtained as the final cost function value 53 and the corresponding motion coefficients as the final motion coefficients 52.
  • the system for motion estimation according to the present invention achieves motion estimation with significantly less prediction error than that of previous solutions, while keeping the computational complexity at a statistically low level. This is achieved by the arrangement enabling the dynamic switching between statistically valid assumptions varying in strength, via assumption verification at each iteration.
  • the present invention specifically provides an improved system for motion estimation of an image segment, using a polynomial motion vector field model.
  • Motion estimation is an element of video coding using motion compensated prediction.
  • the system finds motion coefficients which yield lower prediction error than the prior art solutions, while keeping the computational complexity low. This low prediction error results in better performance in terms of video compression.
  • the low prediction error is achieved by incorporating the general characteristics of image data and motion into a combination of well known function minimization techniques.

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Abstract

La présente invention concerne une technique d'estimation du mouvements et un système pour codeur vidéo. Ce système comprend une entrée pour l'image vidéo (6) à coder ainsi qu'une série d'estimateurs de mouvements de complexité variable qui permettent d'estimer un champ vectoriel de mouvements entre l'image reçue (6) et une image de référence (5). Dans la série d'estimateurs de mouvements, un dispositif (50) permet de sélectionner l'estimateur suivant si l'erreur de prédiction relative au champ vectoriel couvert par l'estimateur de mouvements actuel dépasse un seuil déterminé. Le systèmes comporte également des moyens de lissage (10) et de sous-échantillonnage (20), les uns et les autres étant de préférence interdépendants.
PCT/EP1999/005488 1998-07-29 1999-07-27 Systeme de codage video WO2000007375A1 (fr)

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Publication number Priority date Publication date Assignee Title
US8879631B2 (en) 2007-11-30 2014-11-04 Dolby Laboratories Licensing Corporation Temporally smoothing a motion estimate

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