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Versions: 00 01 02 draft-valin-netvc-pvq

Network Working Group                                          JM. Valin
Internet-Draft                                                   Mozilla
Intended status: Standards Track                            July 4, 2014
Expires: January 5, 2015


              Pyramid Vector Quantization for Video Coding
                     draft-valin-videocodec-pvq-01

Abstract

   This proposes applying pyramid vector quantization (PVQ) to video
   coding.

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   2
   3.  Gain-Shape Coding and Activity Masking  . . . . . . . . . . .   2
   4.  Householder Reflection  . . . . . . . . . . . . . . . . . . .   3
   5.  Angle-Based Encoding  . . . . . . . . . . . . . . . . . . . .   4
   6.  Bi-prediction . . . . . . . . . . . . . . . . . . . . . . . .   5
   7.  Coefficient coding  . . . . . . . . . . . . . . . . . . . . .   6
   8.  Development Repository  . . . . . . . . . . . . . . . . . . .   6
   9.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   6
   10. Security Considerations . . . . . . . . . . . . . . . . . . .   6
   11. Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .   7
   12. References  . . . . . . . . . . . . . . . . . . . . . . . . .   7
     12.1.  Normative References . . . . . . . . . . . . . . . . . .   7
     12.2.  Informative References . . . . . . . . . . . . . . . . .   7
     12.3.  URIs . . . . . . . . . . . . . . . . . . . . . . . . . .   7
   Author's Address  . . . . . . . . . . . . . . . . . . . . . . . .   7

1.  Introduction

   This draft describes a proposal for adapting the Opus RFC 6716
   [RFC6716] energy conservation principle to video coding based on a
   pyramid vector quantizer (PVQ) [PVQ].  One potential advantage of
   conserving energy of the AC coefficients in video coding is
   preserving textures rather than low-passing them.  Also, by
   introducing a fixed-resolution PVQ-type quantizer, we automatically
   gain a simple activity masking model.

   The main challenge of adapting this scheme to video is that we have a
   good prediction (the reference frame), so we are essentially starting
   from a point that is already on the PVQ hyper-sphere, rather than at
   the origin like in CELT.  Other challenges are the introduction of a
   quantization matrix and the fact that we want the reference (motion
   predicted) data to perfectly correspond to one of the entries in our
   codebook.

2.  Terminology

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
   document are to be interpreted as described in RFC 2119 [RFC2119].

3.  Gain-Shape Coding and Activity Masking

   The main idea behind the proposed video coding scheme is to code
   groups of DCT coefficient as a scalar gain and a unit-norm "shape"
   vector.  A block's AC coefficients may all be part of the same group,



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   or may be divided by frequency (e.g. by octave) and/or by
   directionality (horizontal vs vertical).

   It is desirable for a single quality parameter to control the
   resolution of both the gain and the shape.  Ideally, that quality
   parameter should also take into account activity masking, that is,
   the fact that the eye is less sensitive to regions of an image that
   have more details.  According to Jason Garrett-Glaser, the perceptual
   analysis in the x264 encoder uses a resolution proportional to the
   variance of the AC coefficients raised to the power a, with a=0.173.
   For gain-shape quantization, this is equivalent to using a resolution
   of g^(2a), where g is the gain.  We can derive a scalar quantizer
   that follows this resolution:

                                           b
                                g=Q_g gamma     ,

   where gamma is the gain quantization index, b=1/(1-2*a) and Q_g is
   the gain resolution and main quality parameter.

   An important aspect of the current proposal is the use of prediction.
   In the case of the gain, there is usually a significant correlation
   with the gain of neighboring blocks.  One way to predict the gain of
   a block is to compute the gain of the coefficients obtained through
   intra or inter prediction.  Another way is to use the encoded gain of
   the neighboring blocks to explicitly predict the gain of the current
   block.

4.  Householder Reflection

   Let vector x_d denote the (pre-normalization) DCT band to be coded in
   the current block and vector r_d denote the corresponding reference
   (based on intra prediction or motion compensation), the encoder
   computes and encodes the "band gain" g = sqrt(x_d^T x_d).  The
   normalized band is computed as

                                        x_d
                                 x = --------- ,
                                     || x_d ||

   with the normalized reference r similarly computed based on r_d.  The
   encoder then finds the position and sign of the maximum value in r:

                              m = argmax_i | r_i |
                              s = sign(r_m)

   and computes the Householder reflection that reflects r to -s e_m.
   The reflection vector is given by



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                                 v = r + s e_m .

   The encoder reflects the normalized band to find the unit-norm vector

                                        v^T x
                              z = x - 2 -----  v .
                                        v^T v

   The closer the current band is from the reference band, the closer z
   is from -s e_m.  This can be represented either as an angle, or as a
   coordinate on a projected pyramid.

5.  Angle-Based Encoding

   Assuming no quantization, the similarity can be represented by the
   angle

                            theta = arccos(-s z_m) .

   If theta is quantized and transmitted to the decoder, then z can be
   reconstructed as

                    z = -s cos(theta) e_m + sin(theta) z_r ,

   where z_r is a unit vector based on z that excludes dimension m.

   The vector z_r can be quantized using PVQ.  Let y be a vector of
   integers that satisfies

                               sum_i(|y[i]|) = K ,

   with K determined in advance, then the PVQ search finds the vector y
   that maximizes y^T z_r / (y^T y) . The quantized version of z_r is

                                          y
                                z_rq = ------- .
                                       || y ||

   If we assume that MSE is a good criterion for optimizing the
   resolution, then the angle quantization resolution should be
   (roughly)

                                   dg       1        b
                      Q_theta = ---------*----- = ------ .
                                 d(gamma)   g      gamma






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   To derive the optimal K we need to consider the normalized distortion
   for a Laplace-distributed variable found experimentally to be
   approximately

                                  (N-1)^2 + C*(N-1)
                            D_p = ----------------- ,
                                       24*K^2

   with C ~= 4.2.  The distortion due to the gain is

                               b^2*Q_g^2*gamma^(2*b-2)
                         D_g = ----------------------- .
                                        12

   Since PVQ codes N-2 degrees of freedom, its distortion should also be
   (N-2) times the gain distortion, which eventually leads us to the
   optimal number of pulses

                       gamma*sin(theta)       / N + C - 2 \
                   K = ----------------  sqrt | ---------  | .
                               b              \     2     /

   The value of K does not need to be coded because all the variables it
   depends on are known to the decoder.  However, because Q_theta
   depends on the gain, this can lead to unacceptable loss propagation
   behavior in the case where inter prediction is used for the gain.
   This problem can be worked around by making the approximation
   sin(theta)~=theta.  With this approximation, then tau is equal to the
   inverse of the theta quantization index, with no dependency on the
   gain.  Alternatively, instead of quantizing theta, we can quantize
   sin(theta) which also removes the dependency on the gain.  In the
   general case, we quantize f(theta) and then assume that
   sin(theta)~=f(theta).  A possible choice of f(theta) is a quadratic
   function of the form:

                                                       2
                         f(theta) = a1 theta - a2 theta.

   where a1 and a2 are two constants satisfying the constraint that
   f(pi/2)=pi/2.  The value of f(theta) can also be predicted, but in
   case where we care about error propagation, it should only be
   predicted from information coded in the current frame.

6.  Bi-prediction

   We can use this scheme for bi-prediction by introducing a second
   theta parameter.  For the case of two (normalized) reference frames
   r1 and r2, we introduce s1=(r1+r2)/2 and s2=(r1-r2)/2.  We start by



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   using s1 as a reference, apply the Householder reflection to both x
   and s2, and evaluate theta1.  From there, we derive a second
   Householder reflection from the reflected version of s2 and apply it
   to z.  The result is that the theta2 parameter controls how the
   current image compares to the two reference images.  It should even
   be possible to use this in the case of fades, using two references
   that are before the frame being encoded.

7.  Coefficient coding

   Encoding coefficients quantized with PVQ differs from encoding
   scalar-quantized coefficients from the fact that the sum of the
   coefficients magnitude is known (equal to K).  It is possible to take
   advantage of the known K value either through modeling the
   distribution of coefficient magnitude or by modeling the zero runs.
   In the case of magnitude modeling, the expection of the magnitude of
   coefficient n is modeled as

                                               K_n
                           E(|y_n|) = alpha * ----- ,
                                              N - n

   where K_n is the number of number of pulses left after encoding
   coeffients from 0 to n-1 and alpha depends on the distribution of the
   coefficients.  For run-length modeling, the expection of the position
   of the next non-zero coefficient is given by

                                              N - n
                            E(|run|) = beta * ----- ,
                                               K_n

   where beta also models the coefficient distribution.

8.  Development Repository

   The algorithms in this proposal are being developed as part of
   Xiph.Org's Daala project.  The code is available in the Daala git
   repository at [1].  See [2] for more information.

9.  IANA Considerations

   This document makes no request of IANA.

10.  Security Considerations

   This draft has no security considerations.





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11.  Acknowledgements

   Thanks to Jason Garrett-Glaser, Timothy Terriberry, Greg Maxwell, and
   Nathan Egge for their contribution to this document.

12.  References

12.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119, March 1997.

12.2.  Informative References

   [PVQ]      Fischer, T., "A Pyramid Vector Quantizer", IEEE Trans. on
              Information Theory, Vol. 32 pp. 568-583, July 1986.

   [RFC6716]  Valin, JM., Vos, K., and T. Terriberry, "Definition of the
              Opus Audio Codec", RFC 6716, September 2012.

12.3.  URIs

   [1] https://git.xiph.org/daala.git

   [2] https://xiph.org/daala/

Author's Address

   Jean-Marc Valin
   Mozilla
   331 E. Evelyn Avenue
   Mountain View, CA  94041
   USA

   Email: jmvalin@jmvalin.ca
















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