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NETVC (Internet Video Codec)                                      Y. Cho
Internet-Draft                                       Mozilla Corporation
Intended status: Informational                         November 14, 2016
Expires: May 18, 2017


                       Applying PVQ Outside Daala
                      draft-cho-netvc-applypvq-03

Abstract

   This document describes the Perceptual Vector Quantization (PVQ)
   outside of the Daala video codec, where PVQ was originally developed.
   It discusses the issues arising while integrating PVQ into a
   traditional video codec, AV1.

Status of This Memo

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Table of Contents

   1.  Background  . . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Integration of PVQ into non-Daala codec, AV1  . . . . . . . .   3
     2.1.  Signaling Skip for Partition and Transform Block  . . . .   4
     2.2.  Issues  . . . . . . . . . . . . . . . . . . . . . . . . .   5
   3.  Performance of PVQ in AV1 . . . . . . . . . . . . . . . . . .   5
     3.1.  Coding Gain . . . . . . . . . . . . . . . . . . . . . . .   5
     3.2.  Speed . . . . . . . . . . . . . . . . . . . . . . . . . .   7
   4.  Future Work . . . . . . . . . . . . . . . . . . . . . . . . .   8
   5.  Development Repository  . . . . . . . . . . . . . . . . . . .   8
   6.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .   8
   7.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   9
   8.  References  . . . . . . . . . . . . . . . . . . . . . . . . .   9
     8.1.  Informative References  . . . . . . . . . . . . . . . . .   9
     8.2.  URIs  . . . . . . . . . . . . . . . . . . . . . . . . . .   9
   Author's Address  . . . . . . . . . . . . . . . . . . . . . . . .  10

1.  Background

   Perceptual Vector Quantization (PVQ)
   [Perceptual-VQ][I-D.valin-netvc-pvq] has been proposed as a
   quantization and coefficient coding tool for an internet video codec.
   PVQ was originally developed for the Daala video codec [1]
   [PVQ-demo], which does a gain-shape coding of transform coefficients
   instead of more traditional scalar quantization.  (The original
   abbreviation of PVQ, "Pyramid Vector Quantizer", as in
   [I-D.valin-netvc-pvq] is now commonly expanded as "Perceptual Vector
   Quantization".)

   The most distinguishing idea of PVQ is the way it references a
   predictor.  With PVQ, we do not subtract the predictor from the input
   to produce a residual, which is then transformed and coded.  Both the
   predictor and the input are transformed into the frequency domain.
   Then, PVQ applies a reflection to both the predictor and the input
   such that the prediction vector lies on one of the coordinate axes,
   and codes the angle between them.  By not subtracting the predictor
   from the input, the gain of the predictor can be preserved and is
   explicitly coded, which is one of the benefits of PVQ.  Since DC is
   not quantized by PVQ, the gain can be viewed as the amount of
   contrast in an image, which is an important perceptual parameter.

   Also, an input block of transform coefficients is split into
   frequency bands based on their spatial orientation and scale.  Then,
   each band is quantized by PVQ separately.  The 'gain' of a band
   indicates the amount of contrast in the corresponding orientation and
   scale.  It is simply the L2 norm of the band.  The gain is non-
   linearly companded and then scalar quantized and coded.  The



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   remaining information in the band, the 'shape', is then defined as a
   point on the surface of a unit hypersphere.

   Another benefit of PVQ is activity masking based on the gain, which
   automatically controls the quantization resolution based on the image
   contrast without any signaling.  For example, for a smooth image area
   (i.e. low contrast thus low gain), the resolution of quantization
   will increase, thus fewer quantization errors will be shown.  A
   succinct summary on the benefits of PVQ can be found in the
   Section 2.4 of [Terriberry_16].

   Since PVQ has only been used in the Daala video codec, which contains
   many non-traditional design elements, there has not been any chance
   to see the relative coding performance of PVQ compared to scalar
   quantization in a more traditional codec design.  We have tried to
   apply PVQ in the AV1 video codec, which is currently being developed
   by Alliance for Open Media (AOM) as an open source and royalty-free
   video codec.  While most of benefits of using PVQ arise from the
   enhancement of subjective quality of video, compression results with
   activity masking enabled are not available yet in this draft because
   the required parameters, which were set for Daala, have not been
   adjusted to AV1 yet.  These results were achieved optimizing solely
   for PSNR.

2.  Integration of PVQ into non-Daala codec, AV1

   Adopting PVQ in AV1 requires replacing both the scalar quantization
   step and the coefficient coding of AV1 with those of PVQ.  In terms
   of inputs to PVQ and the usage of transforms as shown in Figure 1 and
   Figure 2, the biggest conceptual changes required in a traditional
   coding system, such as AV1, are

   o  Introduction of a transformed predictor both in encoder and
      decoder.  For this, we apply a forward transform to the
      predictors, both intra-predicted pixels and inter-predicted (i.e.,
      motion-compensated) pixels.  This is because PVQ references the
      predictor in the transform domain, instead of using a pixel-domain
      residual as in traditional scalar quantization.

   o  Absence of a difference signal (i.e. residue) defined as "input
      source - predictor".  Hence AV1 with PVQ does not do any
      'subtraction' in order for an input to reference the predictor.
      Instead, PVQ takes a different approach to referencing the
      predictor which happens in the transform domain.







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         input X --> +-------------+                 +-------------+
                     | Subtraction | --> residue --> | Transform T |
       predictor --> +-------------+     signal R    +-------------+
               P                                            |
            |                                               v
            v                                              T(R)
           [+]--> decoded X                                 |
            ^                                               |
            |                                               v
            |       +-----------+    +-----------+     +-----------+
       decoded  <-- | Inverse   | <--| Inverse   | <-- | Scalar    |
             R      | Transform |    | Quantizer |  |  | Quantizer |
                    +-----------+    +-----------+  |  +-----------+
                                                    v
                                              +-------------+
                                bitstream  <--| Coefficient |
                                of coded T(R) |       Coder |
                                              +-------------+

      Figure 1: Traditional architecture containing Quantization and
                                Transforms

               +-------------+            +-----------+
     input X-->| Transform T |--> T(X)--> | PVQ       |
               |_____________|            | Quantizer |  +-------------+
                                   +----> +-----------+  | PVQ         |
               +-------------+     |            |------> | Coefficient |
   predictor-->| Transform T |--> T(P)          v        | Coder       |
           P   |_____________|     |      +-----------+  +-------------+
                                   |      | PVQ       |        |
                                   +----> | Inverse   |        v
                                          | Quantizer |    bitstream
                                          +-----------+    of coded T(X)
                                                 |
                 +-----------+                   v
    decoded X <--| Inverse   | <--------- dequantized T(X)
                 | Transform |
                 +-----------+

                          Figure 2: AV1 with PVQ

2.1.  Signaling Skip for Partition and Transform Block

   In AV1, a skip flag for a partition block is true if all of the
   quantized coefficients in the partition are zeros.  The signaling for
   the prediction mode in a partition cannot be skipped.  If the skip
   flag is true with PVQ, the predicted pixels are the final decoded




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   pixels (plus frame wise in-loop filtering such as deblocking) as in
   AV1 then a forward transform of a predictor is not required.

   While AV1 currently defines only one 'skip' flag for each 'partition'
   (a unit where prediction is done), PVQ introduces another kind of
   'skip' flag, called 'ac_dc_coded', which is defined for each
   transform block (and thus for each Y'CbCr plane as well).  AV1 allows
   that a transform size can be smaller than a partition size which
   leads to partitions that can have multiple transform blocks.  The
   ac_dc_coded flag signals whether DC and/or whole AC coefficients are
   coded by PVQ or not (PVQ does not quantize DC itself though).

2.2.  Issues

   o  PVQ has its own rate-distortion optimization (RDO) that differs
      from that of traditional scalar quantization.  This leads the
      balance of quality between luma and chroma to be different from
      that of scalar quantization.  When scalar quantization of AV1 is
      done for a block of coefficients, RDO, such as trellis coding, can
      be optionally performed.  The second pass of 2-pass encoding in
      AV1 currently uses trellis coding.  When doing so it appears a
      different scaling factor is applied for each of Y'CbCr channels.

   o  In AV1, to optimize speed, there are inverse transforms that can
      skip applying certain 1D basis functions based on the distribution
      of quantized coefficients.  However, this is mostly not possible
      with PVQ since the inverse transform is applied directly to a
      dequantized input, instead of a dequantized difference (i.e. input
      source - predictor) as in traditional video codec.  This is true
      for both encoder and decoder.

   o  PVQ was originally designed for the 2D DCT, while AV1 also uses a
      hybrid 2D transform consisting of a 1D DCT and a 1D ADST.  This
      requires PVQ to have new coefficient scanning orders for the two
      new 2D transforms, DCT-ADST and ADST-DCT (ADST-ADST uses the same
      scan order as for DCT-DCT).  Those new scan orders have been
      produced based on that of AV1, for each PVQ-defined-band of new 2D
      transforms.

3.  Performance of PVQ in AV1

3.1.  Coding Gain

   With the encoding options specified by both NETVC ([2]) and AOM
   testing for the high latency case, PVQ gives similar coding
   efficiency to that of AV1, which is measured in PSNR BD-rate.  Again,
   PVQ's activity masking is not turned on for this testing.  Also,




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   scalar quantization has matured over decades, while video coding with
   PVQ is much more recent.

   We compare the coding efficiency for the IETF test sequence set
   "objective-1-fast" defined in [3], which consists of sixteen of
   1080p, seven of 720p, and seven of 640x360 sequences of various types
   of content, including slow/high motion of people and objects,
   animation, computer games and screen casting.  The encoding is done
   for the first 30 frames of each sequence.  The encoding options used
   is : "-end-usage=q -cq-level=x --passes=2 --good --cpu-used=0 --auto-
   alt-ref=2 --lag-in-frames=25 --limit=30", which is official test
   condition of IETF and AOM for high latency encoding except limiting
   30 frames only.

   For comparison reasons, some of the lambda values used in RDO are
   adjusted to match the balance of luma and chroma quality of the PVQ-
   enabled AV1 to that of current AV1.

   o  Use half the value of lambda during intra prediction for the
      chroma channels.

   o  Scale PVQ's lambda by 0.9 for the chroma channels.

   o  Do not do RDO of DC for the chroma channels.

   The results are shown in Table 1, which is the BD-Rate change for
   several image quality metrics.  (The encoders used to generate this
   result are available from the author's git repository [4] and AOM's
   repository [5].)

                   +-----------+----------------------+
                   |   Metric  | AV1 --> AV1 with PVQ |
                   +-----------+----------------------+
                   |    PSNR   |        -0.17%        |
                   |           |                      |
                   |  PSNR-HVS |        0.27%         |
                   |           |                      |
                   |    SSIM   |        0.93%         |
                   |           |                      |
                   |  MS-SSIM  |        0.14%         |
                   |           |                      |
                   | CIEDE2000 |        -0.28%        |
                   +-----------+----------------------+

                    Table 1: Coding Gain by PVQ in AV1






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3.2.  Speed

   Total encoding time increases roughly 20 times or more when intensive
   RDO options, such as "--passes=2 --good --cpu-used=0 --auto-alt-ref=2
   --lag-in-frames=25", are enabled.  The significant increase in
   encoding time is due to the increase of computation by the PVQ.  The
   PVQ tries to find asymptotically-optimal codepoints (in RD
   optimization sense) on a hypersphere with a greedy search, which has
   time complexity close to O(n*n) for n coefficients.  Meanwhile,
   scalar quantization has time complexity of O(n).

   Compared to Daala, the search space for a RDO decision in AV1 is far
   larger because AV1 considers ten intra prediction modes and four
   different transforms (for the transform block sizes 4x4, 8x8, and
   16x16 only), and the transform block size can be smaller than the
   prediction block size.  Since the largest transform and the
   prediction sizes are currently 32x32 and 64x64 in AV1, PVQ can be
   called approximately 5,160 times more in AV1 than in Daala.  Also,
   AV1 applies transform and quantization for each candidate of RDO.

   As an example, AV1 calls the PVQ function 632,520 times to encode the
   grandma_qcif (176x144) clip in intra frame mode while Daala calls
   3843 times only (for QP = 30 and 39 for AV1 and daala respectively,
   which corresponds to actual quantizer used for quantization being
   38).  So, PVQ was called 165 times more in AV1 than Daala.

   Table 2 shows the frequency of function calls to PVQ and scalar
   quantizers in AV1 at each speed level (where AV1 encoding mode is
   'good') for the same sequence and the QP as used in the above
   example.  The first column indicates speed level, the second column
   shows the number of calls to PVQ's search inside each band (function
   pvq_search_rdo_double() in [6]), the third column shows the number of
   calls to PVQ quantization of a transform block (function
   od_pvq_encode() in [7]), and the fourth column shows the number of
   calls to AV1's block quantizer.  Smaller speed level gives slower
   encoding but better quality for the same rate by doing more RDO
   optimizations.  The major difference from speed level 4 to 3 is
   enabling a use of the transform block smaller than the prediction
   (i.e. partition) block.












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   +--------+-----------------+-----------------+----------------------+
   | Speed  |  # of calls to  |  # of calls to  |  # of calls to PVQ   |
   | Level  |  AV1 quantizer  |  PVQ quantizer  | search inside a band |
   +--------+-----------------+-----------------+----------------------+
   |   5    |      28,028     |      26,786     |       365,913        |
   |        |                 |                 |                      |
   |   4    |      57,445     |      56,980     |       472,222        |
   |        |                 |                 |                      |
   |   3    |     505,039     |     564,724     |      3,680,366       |
   |        |                 |                 |                      |
   |   2    |     505,039     |     564,724     |      3,680,366       |
   |        |                 |                 |                      |
   |   1    |     535,100     |     580,566     |      3,990,327       |
   |        |                 |                 |                      |
   |   0    |     589,931     |     632,520     |      4,109,113       |
   +--------+-----------------+-----------------+----------------------+

        Table 2: Comparison of Frequency of Calls to PVQ and Scalar
                             Quantizers in AV1

4.  Future Work

   Possible future work includes:

   o  Enable activity masking, which also needs a HVS-tuned quantization
      matrix (bandwise QP scalars).

   o  Adjust the balance between luma and chroma qualities, tuning for
      subjective quality.

   o  Optimize the speed of the PVQ code, adding SIMD.

   o  RDO with more model-driven decision making, instead of full
      transform + quantization.

5.  Development Repository

   The ongoing work of integrating PVQ into AV1 video codec is located
   at the git repository [8].

6.  Acknowledgements

   Thanks to Tim Terriberry for his proofreading and valuable comments.
   Also thanks to Guillaume Martres for his contributions to integrating
   PVQ into AV1 during his internship at Mozilla and Thomas Daede for
   providing and maintaining the testing infrastructure by way of the
   www.arewecompressedyet.com (AWCY) website.  [9].




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7.  IANA Considerations

   This memo includes no request to IANA.

8.  References

8.1.  Informative References

   [I-D.valin-netvc-pvq]
              Valin, J., "Pyramid Vector Quantization for Video Coding",
              draft-valin-netvc-pvq-00 (work in progress), June 2015.

   [Perceptual-VQ]
              Valin, JM. and TB. Terriberry, "Perceptual Vector
              Quantization for Video Coding", Proceedings of SPIE Visual
              Information Processing and Communication , February 2015,
              <https://arxiv.org/pdf/1602.05209v1.pdf>.

   [PVQ-demo]
              Valin, JM., "Daala: Perceptual Vector Quantization (PVQ)",
              November 2014, <https://people.xiph.org/~jm/daala/
              pvq_demo/>.

   [Terriberry_16]
              Terriberry, TB., "Perceptually-Driven Video Coding with
              the Daala Video Codec", Proceedings SPIE Volume 9971,
              Applications of Digital Image Processing XXXIX , September
              2016, <https://arxiv.org/pdf/1610.02488.pdf>.

8.2.  URIs

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

   [2] https://tools.ietf.org/html/draft-ietf-netvc-testing-03

   [3] https://tools.ietf.org/html/draft-ietf-netvc-testing-03

   [4] https://github.com/ycho/aom/
       commit/2478029a9b6d02ee2ccc9dbafe7809b5ef345814

   [5] https://aomedia.googlesource.com/
       aom/+/59848c5c797ddb6051e88b283353c7562d3a2c24

   [6] https://github.com/ycho/aom/blob/14981eebb4a08f74182cea3c17f7361b
       c79cf04f/av1/encoder/pvq_encoder.c#L84

   [7] https://github.com/ycho/aom/blob/14981eebb4a08f74182cea3c17f7361b
       c79cf04f/av1/encoder/pvq_encoder.c#L763



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   [8] https://github.com/ycho/aom/tree/av1_pvq

   [9] https://arewecompressedyet.com/

Author's Address

   Yushin Cho
   Mozilla Corporation
   331 E. Evelyn Avenue
   Mountain View, CA  94041
   USA

   Phone: +1 650 903 0800
   Email: ycho@mozilla.com





































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