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Versions: (draft-daede-netvc-testing) 00 01 02 03 04 05

Network Working Group                                           T. Daede
Internet-Draft                                                   Mozilla
Intended status: Informational                                 A. Norkin
Expires: September 28, 2017                                      Netflix
                                                          I. Brailovskiy
                                                           Amazon Lab126
                                                          March 27, 2017


              Video Codec Testing and Quality Measurement
                      draft-ietf-netvc-testing-05

Abstract

   This document describes guidelines and procedures for evaluating a
   video codec.  This covers subjective and objective tests, test
   conditions, and materials used for the test.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF).  Note that other groups may also distribute
   working documents as Internet-Drafts.  The list of current Internet-
   Drafts is at http://datatracker.ietf.org/drafts/current/.

   Internet-Drafts are draft documents valid for a maximum of six months
   and may be updated, replaced, or obsoleted by other documents at any
   time.  It is inappropriate to use Internet-Drafts as reference
   material or to cite them other than as "work in progress."

   This Internet-Draft will expire on September 28, 2017.

Copyright Notice

   Copyright (c) 2017 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents
   (http://trustee.ietf.org/license-info) in effect on the date of
   publication of this document.  Please review these documents
   carefully, as they describe your rights and restrictions with respect
   to this document.  Code Components extracted from this document must
   include Simplified BSD License text as described in Section 4.e of




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   the Trust Legal Provisions and are provided without warranty as
   described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Subjective quality tests  . . . . . . . . . . . . . . . . . .   3
     2.1.  Still Image Pair Comparison . . . . . . . . . . . . . . .   3
     2.2.  Video Pair Comparison . . . . . . . . . . . . . . . . . .   3
     2.3.  Subjective viewing test . . . . . . . . . . . . . . . . .   4
   3.  Objective Metrics . . . . . . . . . . . . . . . . . . . . . .   4
     3.1.  Overall PSNR  . . . . . . . . . . . . . . . . . . . . . .   4
     3.2.  Frame-averaged PSNR . . . . . . . . . . . . . . . . . . .   5
     3.3.  PSNR-HVS-M  . . . . . . . . . . . . . . . . . . . . . . .   5
     3.4.  SSIM  . . . . . . . . . . . . . . . . . . . . . . . . . .   5
     3.5.  Multi-Scale SSIM  . . . . . . . . . . . . . . . . . . . .   5
     3.6.  CIEDE2000 . . . . . . . . . . . . . . . . . . . . . . . .   5
     3.7.  VMAF  . . . . . . . . . . . . . . . . . . . . . . . . . .   6
   4.  Comparing and Interpreting Results  . . . . . . . . . . . . .   6
     4.1.  Graphing  . . . . . . . . . . . . . . . . . . . . . . . .   6
     4.2.  BD-Rate . . . . . . . . . . . . . . . . . . . . . . . . .   6
     4.3.  Ranges  . . . . . . . . . . . . . . . . . . . . . . . . .   7
   5.  Test Sequences  . . . . . . . . . . . . . . . . . . . . . . .   7
     5.1.  Sources . . . . . . . . . . . . . . . . . . . . . . . . .   7
     5.2.  Test Sets . . . . . . . . . . . . . . . . . . . . . . . .   8
       5.2.1.  regression-1  . . . . . . . . . . . . . . . . . . . .   8
       5.2.2.  objective-2-slow  . . . . . . . . . . . . . . . . . .   8
       5.2.3.  objective-2-fast  . . . . . . . . . . . . . . . . . .  12
       5.2.4.  objective-1.1 . . . . . . . . . . . . . . . . . . . .  14
       5.2.5.  objective-1-fast  . . . . . . . . . . . . . . . . . .  17
     5.3.  Operating Points  . . . . . . . . . . . . . . . . . . . .  18
       5.3.1.  Common settings . . . . . . . . . . . . . . . . . . .  18
       5.3.2.  High Latency CQP  . . . . . . . . . . . . . . . . . .  19
       5.3.3.  Low Latency CQP . . . . . . . . . . . . . . . . . . .  19
       5.3.4.  Unconstrained High Latency  . . . . . . . . . . . . .  19
       5.3.5.  Unconstrained Low Latency . . . . . . . . . . . . . .  19
   6.  Automation  . . . . . . . . . . . . . . . . . . . . . . . . .  20
     6.1.  Regression tests  . . . . . . . . . . . . . . . . . . . .  20
     6.2.  Objective performance tests . . . . . . . . . . . . . . .  20
     6.3.  Periodic tests  . . . . . . . . . . . . . . . . . . . . .  21
   7.  Informative References  . . . . . . . . . . . . . . . . . . .  21
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  23

1.  Introduction

   When developing a video codec, changes and additions to the codec
   need to be decided based on their performance tradeoffs.  In
   addition, measurements are needed to determine when the codec has met



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   its performance goals.  This document specifies how the tests are to
   be carried about to ensure valid comparisons when evaluating changes
   under consideration.  Authors of features or changes should provide
   the results of the appropriate test when proposing codec
   modifications.

2.  Subjective quality tests

   Subjective testing is the preferable method of testing video codecs.

   Subjective testing results take priority over objective testing
   results, when available.  Subjective testing is recommended
   especially when taking advantage of psychovisual effects that may not
   be well represented by objective metrics, or when different objective
   metrics disagree.

   Selection of a testing methodology depends on the feature being
   tested and the resources available.  Test methodologies are presented
   in order of increasing accuracy and cost.

   Testing relies on the resources of participants.  For this reason,
   even if the group agrees that a particular test is important, if no
   one volunteers to do it, or if volunteers do not complete it in a
   timely fashion, then that test should be discarded.  This ensures
   that only important tests be done in particular, the tests that are
   important to participants.

2.1.  Still Image Pair Comparison

   A simple way to determine superiority of one compressed image is to
   visually compare two compressed images, and have the viewer judge
   which one has a higher quality.  This is used for rapid comparisons
   during development - the viewer may be a developer or user, for
   example.  Because testing is done on still images (keyframes), this
   is only suitable for changes with similar or no effect on other
   frames.  For example, this test may be suitable for an intra de-
   ringing filter, but not for a new inter prediction mode.  For this
   test, the two compressed images should have similar compressed file
   sizes, with one image being no more than 5% larger than the other.
   In addition, at least 5 different images should be compared.

2.2.  Video Pair Comparison

   Video comparisons are necessary when making changes with temporal
   effects, such as changes to inter-frame prediction.  Video pair
   comparisons follow the same procedure as still images.





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2.3.  Subjective viewing test

   A subjective viewing test is the preferred method of evaluating the
   quality.  The subjective test should be performed as either
   consecutively showing the video sequences on one screen or on two
   screens located side-by-side.  The testing procedure should normally
   follow rules described in [BT500] and be performed with non-expert
   test subjects.  The result of the test could be (depending on the
   test procedure) mean opinion scores (MOS) or differential mean
   opinion scores (DMOS).  Normally, confidence intervals are also
   calculated to judge whether the difference between two encodings is
   statistically significant.  In certain cases, a viewing test with
   expert test subjects can be performed, for example if a test should
   evaluate technologies with similar performance with respect to a
   particular artifact (e.g. loop filters or motion prediction).
   Depending on the setup of the test, the output could be a MOS, DMOS
   or a percentage of experts, who preferred one or another technology.

3.  Objective Metrics

   Objective metrics are used in place of subjective metrics for easy
   and repeatable experiments.  Most objective metrics have been
   designed to correlate with subjective scores.

   The following descriptions give an overview of the operation of each
   of the metrics.  Because implementation details can sometimes vary,
   the exact implementation is specified in C in the Daala tools
   repository [DAALA-GIT].  Implementations of metrics must directly
   support the input's resolution, bit depth, and sampling format.

   Unless otherwise specified, all of the metrics described below only
   apply to the luma plane, individually by frame.  When applied to the
   video, the scores of each frame are averaged to create the final
   score.

   Codecs must output the same resolution, bit depth, and sampling
   format as the input.

3.1.  Overall PSNR

   PSNR is a traditional signal quality metric, measured in decibels.
   It is directly drived from mean square error (MSE), or its square
   root (RMSE).  The formula used is:

   20 * log10 ( MAX / RMSE )

   or, equivalently:




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   10 * log10 ( MAX^2 / MSE )

   where the error is computed over all the pixels in the video, which
   is the method used in the dump_psnr.c reference implementation.

   This metric may be applied to both the luma and chroma planes, with
   all planes reported separately.

3.2.  Frame-averaged PSNR

   PSNR can also be calculated per-frame, and then the values averaged
   together.  This is reported in the same way as overall PSNR.

3.3.  PSNR-HVS-M

   The PSNR-HVS metric performs a DCT transform of 8x8 blocks of the
   image, weights the coefficients, and then calculates the PSNR of
   those coefficients.  Several different sets of weights have been
   considered.  [PSNRHVS] The weights used by the dump_pnsrhvs.c tool in
   the Daala repository have been found to be the best match to real MOS
   scores.

3.4.  SSIM

   SSIM (Structural Similarity Image Metric) is a still image quality
   metric introduced in 2004 [SSIM].  It computes a score for each
   individual pixel, using a window of neighboring pixels.  These scores
   can then be averaged to produce a global score for the entire image.
   The original paper produces scores ranging between 0 and 1.

   To linearize the metric for BD-Rate computation, the score is
   converted into a nonlinear decibel scale:

   -10 * log10 (1 - SSIM)

3.5.  Multi-Scale SSIM

   Multi-Scale SSIM is SSIM extended to multiple window sizes [MSSSIM].
   The metric score is converted to decibels in the same way as SSIM.

3.6.  CIEDE2000

   CIEDE2000 is a metric based on CIEDE color distances [CIEDE2000].  It
   generates a single score taking into account all three chroma planes.
   It does not take into consideration any structural similarity or
   other psychovisual effects.





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3.7.  VMAF

   Video Multi-method Assessment Fusion (VMAF) is a full-reference
   perceptual video quality metric that aims to approximate human
   perception of video quality [VMAF].  This metric is focused on
   quality degradation due compression and rescaling.  VMAF estimates
   the perceived quality score by computing scores from multiple quality
   assessment algorithms, and fusing them using a support vector machine
   (SVM).  Currently, three image fidelity metrics and one temporal
   signal have been chosen as features to the SVM, namely Anti-noise SNR
   (ANSNR), Detail Loss Measure (DLM), Visual Information Fidelity
   (VIF), and the mean co-located pixel difference of a frame with
   respect to the previous frame.

   The quality score from VMAF is used directly to calculate BD-Rate,
   without any conversions.

4.  Comparing and Interpreting Results

4.1.  Graphing

   When displayed on a graph, bitrate is shown on the X axis, and the
   quality metric is on the Y axis.  For publication, the X axis should
   be linear.  The Y axis metric should be plotted in decibels.  If the
   quality metric does not natively report quality in decibels, it
   should be converted as described in the previous section.

4.2.  BD-Rate

   The Bjontegaard rate difference, also known as BD-rate, allows the
   measurement of the bitrate reduction offered by a codec or codec
   feature, while maintaining the same quality as measured by objective
   metrics.  The rate change is computed as the average percent
   difference in rate over a range of qualities.  Metric score ranges
   are not static - they are calculated either from a range of bitrates
   of the reference codec, or from quantizers of a third, anchor codec.
   Given a reference codec and test codec, BD-rate values are calculated
   as follows:

   o  Rate/distortion points are calculated for the reference and test
      codec.

      *  At least four points must be computed.  These points should be
         the same quantizers when comparing two versions of the same
         codec.

      *  Additional points outside of the range should be discarded.




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   o  The rates are converted into log-rates.

   o  A piecewise cubic hermite interpolating polynomial is fit to the
      points for each codec to produce functions of log-rate in terms of
      distortion.

   o  Metric score ranges are computed:

      *  If comparing two versions of the same codec, the overlap is the
         intersection of the two curves, bound by the chosen quantizer
         points.

      *  If comparing dissimilar codecs, a third anchor codec's metric
         scores at fixed quantizers are used directly as the bounds.

   o  The log-rate is numerically integrated over the metric range for
      each curve, using at least 1000 samples and trapezoidal
      integration.

   o  The resulting integrated log-rates are converted back into linear
      rate, and then the percent difference is calculated from the
      reference to the test codec.

4.3.  Ranges

   For individual feature changes in libaom or libvpx, the overlap BD-
   Rate method with quantizers 20, 32, 43, and 55 must be used.

   For the final evaluation described in [I-D.ietf-netvc-requirements],
   the quantizers used are 20, 24, 28, 32, 36, 39, 43, 47, 51, and 55.

5.  Test Sequences

5.1.  Sources

   Lossless test clips are preferred for most tests, because the
   structure of compression artifacts in already-compressed clips may
   introduce extra noise in the test results.  However, a large amount
   of content on the internet needs to be recompressed at least once, so
   some sources of this nature are useful.  The encoder should run at
   the same bit depth as the original source.  In addition, metrics need
   to support operation at high bit depth.  If one or more codecs in a
   comparison do not support high bit depth, sources need to be
   converted once before entering the encoder.







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5.2.  Test Sets

   Sources are divided into several categories to test different
   scenarios the codec will be required to operate in.  For easier
   comparison, all videos in each set should have the same color
   subsampling, same resolution, and same number of frames.  In
   addition, all test videos must be publicly available for testing use,
   to allow for reproducibility of results.  All current test sets are
   available for download [TESTSEQUENCES].

   Test sequences should be downloaded in whole.  They should not be
   recreated from the original sources.

5.2.1.  regression-1

   This test set is used for basic regression testing.  It contains a
   very small number of clips.

   o  kirlandvga (640x360, 8bit, 4:2:0, 300 frames)

   o  FourPeople (1280x720, 8bit, 4:2:0, 60 frames)

   o  Narrarator (4096x2160, 10bit, 4:2:0, 15 frames)

   o  CSGO (1920x1080, 8bit, 4:4:4 60 frames)

5.2.2.  objective-2-slow

   This test set is a comprehensive test set, grouped by resolution.
   These test clips were created from originals at [TESTSEQUENCES].
   They have been scaled and cropped to match the resolution of their
   category.  This test set requires compiling with high bit depth
   support.

   4096x2160, 4:2:0, 60 frames:

   o  Netflix_BarScene_4096x2160_60fps_10bit_420_60f

   o  Netflix_BoxingPractice_4096x2160_60fps_10bit_420_60f

   o  Netflix_Dancers_4096x2160_60fps_10bit_420_60f

   o  Netflix_Narrator_4096x2160_60fps_10bit_420_60f

   o  Netflix_RitualDance_4096x2160_60fps_10bit_420_60f

   o  Netflix_ToddlerFountain_4096x2160_60fps_10bit_420_60f




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   o  Netflix_WindAndNature_4096x2160_60fps_10bit_420_60f

   o  street_hdr_amazon_2160p

   1920x1080, 4:2:0, 60 frames:

   o  aspen_1080p_60f

   o  crowd_run_1080p50_60f

   o  ducks_take_off_1080p50_60f

   o  guitar_hdr_amazon_1080p

   o  life_1080p30_60f

   o  Netflix_Aerial_1920x1080_60fps_8bit_420_60f

   o  Netflix_Boat_1920x1080_60fps_8bit_420_60f

   o  Netflix_Crosswalk_1920x1080_60fps_8bit_420_60f

   o  Netflix_FoodMarket_1920x1080_60fps_8bit_420_60f

   o  Netflix_PierSeaside_1920x1080_60fps_8bit_420_60f

   o  Netflix_SquareAndTimelapse_1920x1080_60fps_8bit_420_60f

   o  Netflix_TunnelFlag_1920x1080_60fps_8bit_420_60f

   o  old_town_cross_1080p50_60f

   o  pan_hdr_amazon_1080p

   o  park_joy_1080p50_60f

   o  pedestrian_area_1080p25_60f

   o  rush_field_cuts_1080p_60f

   o  rush_hour_1080p25_60f

   o  seaplane_hdr_amazon_1080p

   o  station2_1080p25_60f

   o  touchdown_pass_1080p_60f




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   1280x720, 4:2:0, 120 frames:

   o  boat_hdr_amazon_720p

   o  dark720p_120f

   o  FourPeople_1280x720_60_120f

   o  gipsrestat720p_120f

   o  Johnny_1280x720_60_120f

   o  KristenAndSara_1280x720_60_120f

   o  Netflix_DinnerScene_1280x720_60fps_8bit_420_120f

   o  Netflix_DrivingPOV_1280x720_60fps_8bit_420_120f

   o  Netflix_FoodMarket2_1280x720_60fps_8bit_420_120f

   o  Netflix_RollerCoaster_1280x720_60fps_8bit_420_120f

   o  Netflix_Tango_1280x720_60fps_8bit_420_120f

   o  rain_hdr_amazon_720p

   o  vidyo1_720p_60fps_120f

   o  vidyo3_720p_60fps_120f

   o  vidyo4_720p_60fps_120f

   640x360, 4:2:0, 120 frames:

   o  blue_sky_360p_120f

   o  controlled_burn_640x360_120f

   o  desktop2360p_120f

   o  kirland360p_120f

   o  mmstationary360p_120f

   o  niklas360p_120f

   o  rain2_hdr_amazon_360p




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   o  red_kayak_360p_120f

   o  riverbed_360p25_120f

   o  shields2_640x360_120f

   o  snow_mnt_640x360_120f

   o  speed_bag_640x360_120f

   o  stockholm_640x360_120f

   o  tacomanarrows360p_120f

   o  thaloundeskmtg360p_120f

   o  water_hdr_amazon_360p

   426x240, 4:2:0, 120 frames:

   o  bqfree_240p_120f

   o  bqhighway_240p_120f

   o  bqzoom_240p_120f

   o  chairlift_240p_120f

   o  dirtbike_240p_120f

   o  mozzoom_240p_120f

   1920x1080, 4:4:4 or 4:2:0, 60 frames:

   o  CSGO_60f.y4m

   o  DOTA2_60f_420.y4m

   o  MINECRAFT_60f_420.y4m

   o  STARCRAFT_60f_420.y4m

   o  EuroTruckSimulator2_60f.y4m

   o  Hearthstone_60f.y4m

   o  wikipedia_420.y4m




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   o  pvq_slideshow.y4m

5.2.3.  objective-2-fast

   This test set is a strict subset of objective-2-slow.  It is designed
   for faster runtime.  This test set requires compiling with high bit
   depth support.

   1920x1080, 4:2:0, 60 frames:

   o  aspen_1080p_60f

   o  ducks_take_off_1080p50_60f

   o  life_1080p30_60f

   o  Netflix_Aerial_1920x1080_60fps_8bit_420_60f

   o  Netflix_Boat_1920x1080_60fps_8bit_420_60f

   o  Netflix_FoodMarket_1920x1080_60fps_8bit_420_60f

   o  Netflix_PierSeaside_1920x1080_60fps_8bit_420_60f

   o  Netflix_SquareAndTimelapse_1920x1080_60fps_8bit_420_60f

   o  Netflix_TunnelFlag_1920x1080_60fps_8bit_420_60f

   o  rush_hour_1080p25_60f

   o  seaplane_hdr_amazon_1080p

   o  touchdown_pass_1080p_60f

   1280x720, 4:2:0, 120 frames:

   o  boat_hdr_amazon_720p

   o  dark720p_120f

   o  gipsrestat720p_120f

   o  KristenAndSara_1280x720_60_120f

   o  Netflix_DrivingPOV_1280x720_60fps_8bit_420_60f

   o  Netflix_RollerCoaster_1280x720_60fps_8bit_420_60f




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   o  vidyo1_720p_60fps_120f

   o  vidyo4_720p_60fps_120f

   640x360, 4:2:0, 120 frames:

   o  blue_sky_360p_120f

   o  controlled_burn_640x360_120f

   o  kirland360p_120f

   o  niklas360p_120f

   o  rain2_hdr_amazon_360p

   o  red_kayak_360p_120f

   o  riverbed_360p25_120f

   o  shields2_640x360_120f

   o  speed_bag_640x360_120f

   o  thaloundeskmtg360p_120f

   426x240, 4:2:0, 120 frames:

   o  bqfree_240p_120f

   o  bqzoom_240p_120f

   o  dirtbike_240p_120f

   1290x1080, 4:2:0, 60 frames:

   o  DOTA2_60f_420.y4m

   o  MINECRAFT_60f_420.y4m

   o  STARCRAFT_60f_420.y4m

   o  wikipedia_420.y4m








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5.2.4.  objective-1.1

   This test set is an old version of objective-2-slow.

   4096x2160, 10bit, 4:2:0, 60 frames:

   o  Aerial (start frame 600)

   o  BarScene (start frame 120)

   o  Boat (start frame 0)

   o  BoxingPractice (start frame 0)

   o  Crosswalk (start frame 0)

   o  Dancers (start frame 120)

   o  FoodMarket

   o  Narrator

   o  PierSeaside

   o  RitualDance

   o  SquareAndTimelapse

   o  ToddlerFountain (start frame 120)

   o  TunnelFlag

   o  WindAndNature (start frame 120)

   1920x1080, 8bit, 4:4:4, 60 frames:

   o  CSGO

   o  DOTA2

   o  EuroTruckSimulator2

   o  Hearthstone

   o  MINECRAFT

   o  STARCRAFT




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   o  wikipedia

   o  pvq_slideshow

   1920x1080, 8bit, 4:2:0, 60 frames:

   o  ducks_take_off

   o  life

   o  aspen

   o  crowd_run

   o  old_town_cross

   o  park_joy

   o  pedestrian_area

   o  rush_field_cuts

   o  rush_hour

   o  station2

   o  touchdown_pass

   1280x720, 8bit, 4:2:0, 60 frames:

   o  Netflix_FoodMarket2

   o  Netflix_Tango

   o  DrivingPOV (start frame 120)

   o  DinnerScene (start frame 120)

   o  RollerCoaster (start frame 600)

   o  FourPeople

   o  Johnny

   o  KristenAndSara

   o  vidyo1




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   o  vidyo3

   o  vidyo4

   o  dark720p

   o  gipsrecmotion720p

   o  gipsrestat720p

   o  controlled_burn

   o  stockholm

   o  speed_bag

   o  snow_mnt

   o  shields

   640x360, 8bit, 4:2:0, 60 frames:

   o  red_kayak

   o  blue_sky

   o  riverbed

   o  thaloundeskmtgvga

   o  kirlandvga

   o  tacomanarrowsvga

   o  tacomascmvvga

   o  desktop2360p

   o  mmmovingvga

   o  mmstationaryvga

   o  niklasvga








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5.2.5.  objective-1-fast

   This is an old version of objective-2-fast.

   1920x1080, 8bit, 4:2:0, 60 frames:

   o  Aerial (start frame 600)

   o  Boat (start frame 0)

   o  Crosswalk (start frame 0)

   o  FoodMarket

   o  PierSeaside

   o  SquareAndTimelapse

   o  TunnelFlag

   1920x1080, 8bit, 4:2:0, 60 frames:

   o  CSGO

   o  EuroTruckSimulator2

   o  MINECRAFT

   o  wikipedia

   1920x1080, 8bit, 4:2:0, 60 frames:

   o  ducks_take_off

   o  aspen

   o  old_town_cross

   o  pedestrian_area

   o  rush_hour

   o  touchdown_pass

   1280x720, 8bit, 4:2:0, 60 frames:

   o  Netflix_FoodMarket2




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   o  DrivingPOV (start frame 120)

   o  RollerCoaster (start frame 600)

   o  Johnny

   o  vidyo1

   o  vidyo4

   o  gipsrecmotion720p

   o  speed_bag

   o  shields

   640x360, 8bit, 4:2:0, 60 frames:

   o  red_kayak

   o  riverbed

   o  kirlandvga

   o  tacomascmvvga

   o  mmmovingvga

   o  niklasvga

5.3.  Operating Points

   Four operating modes are defined.  High latency is intended for on
   demand streaming, one-to-many live streaming, and stored video.  Low
   latency is intended for videoconferencing and remote access.  Both of
   these modes come in CQP and unconstrained variants.  When testing
   still image sets, such as subset1, high latency CQP mode should be
   used.

5.3.1.  Common settings

   Encoders should be configured to their best settings when being
   compared against each other:

   o  av1: -codec=av1 -ivf -frame-parallel=0 -tile-columns=0 -cpu-used=0
      -threads=1





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5.3.2.  High Latency CQP

   High Latency CQP is used for evaluating incremental changes to a
   codec.  This method is well suited to compare codecs with similar
   coding tools.  It allows codec features with intrinsic frame delay.

   o  daala: -v=x -b 2

   o  vp9: -end-usage=q -cq-level=x -lag-in-frames=25 -auto-alt-ref=2

   o  av1: -end-usage=q -cq-level=x -lag-in-frames=25 -auto-alt-ref=2

5.3.3.  Low Latency CQP

   Low Latency CQP is used for evaluating incremental changes to a
   codec.  This method is well suited to compare codecs with similar
   coding tools.  It requires the codec to be set for zero intrinsic
   frame delay.

   o  daala: -v=x

   o  av1: -end-usage=q -cq-level=x -lag-in-frames=0

5.3.4.  Unconstrained High Latency

   The encoder should be run at the best quality mode available, using
   the mode that will provide the best quality per bitrate (VBR or
   constant quality mode).  Lookahead and/or two-pass are allowed, if
   supported.  One parameter is provided to adjust bitrate, but the
   units are arbitrary.  Example configurations follow:

   o  x264: -crf=x

   o  x265: -crf=x

   o  daala: -v=x -b 2

   o  av1: -end-usage=q -cq-level=x -lag-in-frames=25 -auto-alt-ref=2

5.3.5.  Unconstrained Low Latency

   The encoder should be run at the best quality mode available, using
   the mode that will provide the best quality per bitrate (VBR or
   constant quality mode), but no frame delay, buffering, or lookahead
   is allowed.  One parameter is provided to adjust bitrate, but the
   units are arbitrary.  Example configurations follow:

   o  x264: -crf-x -tune zerolatency



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   o  x265: -crf=x -tune zerolatency

   o  daala: -v=x

   o  av1: -end-usage=q -cq-level=x -lag-in-frames=0

6.  Automation

   Frequent objective comparisons are extremely beneficial while
   developing a new codec.  Several tools exist in order to automate the
   process of objective comparisons.  The Compare-Codecs tool allows BD-
   rate curves to be generated for a wide variety of codecs
   [COMPARECODECS].  The Daala source repository contains a set of
   scripts that can be used to automate the various metrics used.  In
   addition, these scripts can be run automatically utilizing
   distributed computers for fast results, with rd_tool [RD_TOOL].  This
   tool can be run via a web interface called AreWeCompressedYet [AWCY],
   or locally.

   Because of computational constraints, several levels of testing are
   specified.

6.1.  Regression tests

   Regression tests run on a small number of short sequences -
   regression-test-1.  The regression tests should include a number of
   various test conditions.  The purpose of regression tests is to
   ensure bug fixes (and similar patches) do not negatively affect the
   performance.  The anchor in regression tests is the previous revision
   of the codec in source control.  Regression tests are run on both
   high and low latency CQP modes

6.2.  Objective performance tests

   Changes that are expected to affect the quality of encode or
   bitstream should run an objective performance test.  The performance
   tests should be run on a wider number of sequences.  The following
   data should be reported:

   o  Identifying information for the encoder used, such as the git
      commit hash.

   o  Command line options to the encoder, configure script, and
      anything else necessary to replicate the experiment.

   o  The name of the test set run (objective-1)





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   o  For both high and low latency CQP modes, and for each objective
      metric:

      *  The BD-Rate score, in percent, for each clip.

      *  The average of all BD-Rate scores, equally weighted, for each
         resolution category in the test set.

      *  The average of all BD-Rate scores for all videos in all
         categories.

   For non-tool contributions, the test set objective-1-fast can be
   substituted.

6.3.  Periodic tests

   Periodic tests are run on a wide range of bitrates in order to gauge
   progress over time, as well as detect potential regressions missed by
   other tests.

7.  Informative References

   [AWCY]     Xiph.Org, "Are We Compressed Yet?", 2016,
              <https://arewecompressedyet.com/>.

   [BT500]    ITU-R, "Recommendation ITU-R BT.500-13", 2012,
              <https://www.itu.int/dms_pubrec/itu-r/rec/bt/R-REC-
              BT.500-13-201201-I!!PDF-E.pdf>.

   [CIEDE2000]
              Yang, Y., Ming, J., and N. Yu, "Color Image Quality
              Assessment Based on CIEDE2000", 2012,
              <http://dx.doi.org/10.1155/2012/273723>.

   [COMPARECODECS]
              Alvestrand, H., "Compare Codecs", 2015,
              <http://compare-codecs.appspot.com/>.

   [DAALA-GIT]
              Xiph.Org, "Daala Git Repository", 2015,
              <http://git.xiph.org/?p=daala.git;a=summary>.

   [DERFVIDEO]
              Terriberry, T., "Xiph.org Video Test Media", n.d.,
              <https://media.xiph.org/video/derf/>.






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   [FASTSSIM]
              Chen, M. and A. Bovik, "Fast structural similarity index
              algorithm", 2010,
              <http://live.ece.utexas.edu/publications/2011/
              chen_rtip_2011.pdf>.

   [I-D.ietf-netvc-requirements]
              Filippov, A., Norkin, A., and j.
              jose.roberto.alvarez@huawei.com, "<Video Codec
              Requirements and Evaluation Methodology>", draft-ietf-
              netvc-requirements-02 (work in progress), June 2016.

   [L1100]    Bossen, F., "Common test conditions and software reference
              configurations", JCTVC L1100, 2013,
              <http://phenix.int-evry.fr/jct/>.

   [MSSSIM]   Wang, Z., Simoncelli, E., and A. Bovik, "Multi-Scale
              Structural Similarity for Image Quality Assessment", n.d.,
              <http://www.cns.nyu.edu/~zwang/files/papers/msssim.pdf>.

   [PSNRHVS]  Egiazarian, K., Astola, J., Ponomarenko, N., Lukin, V.,
              Battisti, F., and M. Carli, "A New Full-Reference Quality
              Metrics Based on HVS", 2002.

   [RD_TOOL]  Xiph.Org, "rd_tool", 2016, <https://github.com/tdaede/
              rd_tool>.

   [SSIM]     Wang, Z., Bovik, A., Sheikh, H., and E. Simoncelli, "Image
              Quality Assessment: From Error Visibility to Structural
              Similarity", 2004,
              <http://www.cns.nyu.edu/pub/eero/wang03-reprint.pdf>.

   [STEAM]    Valve Corporation, "Steam Hardware & Software Survey: June
              2015", June 2015,
              <http://store.steampowered.com/hwsurvey>.

   [TESTSEQUENCES]
              Daede, T., "Test Sets", n.d.,
              <https://people.xiph.org/~tdaede/sets/>.

   [VMAF]     Aaron, A., Li, Z., Manohara, M., Lin, J., Wu, E., and C.
              Kuo, "VMAF - Video Multi-Method Assessment Fusion", 2015,
              <https://github.com/Netflix/vmaf>.








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Authors' Addresses

   Thomas Daede
   Mozilla

   Email: tdaede@mozilla.com


   Andrey Norkin
   Netflix

   Email: anorkin@netflix.com


   Ilya Brailovskiy
   Amazon Lab126

   Email: brailovs@lab126.com

































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