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Miscellaneous warnings: ---------------------------------------------------------------------------- == The copyright year in the IETF Trust and authors Copyright Line does not match the current year -- The document date (February 29, 2016) is 2973 days in the past. Is this intentional? Checking references for intended status: Informational ---------------------------------------------------------------------------- == Unused Reference: 'L1100' is defined on line 433, but no explicit reference was found in the text == Unused Reference: 'STEAM' is defined on line 450, but no explicit reference was found in the text Summary: 2 errors (**), 0 flaws (~~), 3 warnings (==), 1 comment (--). Run idnits with the --verbose option for more detailed information about the items above. -------------------------------------------------------------------------------- 2 Network Working Group T. Daede 3 Internet-Draft Mozilla 4 Intended status: Informational A. Norkin 5 Expires: September 01, 2016 Netflix 6 February 29, 2016 8 Video Codec Testing and Quality Measurement 9 draft-ietf-netvc-testing-01 11 Abstract 13 This document describes guidelines and procedures for evaluating a 14 video codec specified at the IETF. This covers subjective and 15 objective tests, test conditions, and materials used for the test. 17 Status of This Memo 19 This Internet-Draft is submitted in full conformance with the 20 provisions of BCP 78 and BCP 79. 22 Internet-Drafts are working documents of the Internet Engineering 23 Task Force (IETF). Note that other groups may also distribute 24 working documents as Internet-Drafts. The list of current Internet- 25 Drafts is at http://datatracker.ietf.org/drafts/current/. 27 Internet-Drafts are draft documents valid for a maximum of six months 28 and may be updated, replaced, or obsoleted by other documents at any 29 time. It is inappropriate to use Internet-Drafts as reference 30 material or to cite them other than as "work in progress." 32 This Internet-Draft will expire on September 01, 2016. 34 Copyright Notice 36 Copyright (c) 2016 IETF Trust and the persons identified as the 37 document authors. All rights reserved. 39 This document is subject to BCP 78 and the IETF Trust's Legal 40 Provisions Relating to IETF Documents 41 (http://trustee.ietf.org/license-info) in effect on the date of 42 publication of this document. Please review these documents 43 carefully, as they describe your rights and restrictions with respect 44 to this document. Code Components extracted from this document must 45 include Simplified BSD License text as described in Section 4.e of 46 the Trust Legal Provisions and are provided without warranty as 47 described in the Simplified BSD License. 49 Table of Contents 51 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2 52 2. Subjective quality tests . . . . . . . . . . . . . . . . . . 2 53 2.1. Still Image Pair Comparison . . . . . . . . . . . . . . . 3 54 2.2. Subjective viewing test . . . . . . . . . . . . . . . . . 3 55 2.3. Expert viewing . . . . . . . . . . . . . . . . . . . . . 3 56 3. Objective Metrics . . . . . . . . . . . . . . . . . . . . . . 3 57 3.1. Overall PSNR . . . . . . . . . . . . . . . . . . . . . . 4 58 3.2. Frame-averaged PSNR . . . . . . . . . . . . . . . . . . . 4 59 3.3. PSNR-HVS-M . . . . . . . . . . . . . . . . . . . . . . . 4 60 3.4. SSIM . . . . . . . . . . . . . . . . . . . . . . . . . . 5 61 3.5. Multi-Scale SSIM . . . . . . . . . . . . . . . . . . . . 5 62 3.6. Fast Multi-Scale SSIM . . . . . . . . . . . . . . . . . . 5 63 3.7. CIEDE2000 . . . . . . . . . . . . . . . . . . . . . . . . 5 64 3.8. VMAF . . . . . . . . . . . . . . . . . . . . . . . . . . 5 65 4. Comparing and Interpreting Results . . . . . . . . . . . . . 5 66 4.1. Graphing . . . . . . . . . . . . . . . . . . . . . . . . 5 67 4.2. Bjontegaard . . . . . . . . . . . . . . . . . . . . . . . 6 68 4.3. Ranges . . . . . . . . . . . . . . . . . . . . . . . . . 6 69 5. Test Sequences . . . . . . . . . . . . . . . . . . . . . . . 6 70 5.1. Sources . . . . . . . . . . . . . . . . . . . . . . . . . 6 71 5.2. Test Sets . . . . . . . . . . . . . . . . . . . . . . . . 7 72 5.3. Operating Points . . . . . . . . . . . . . . . . . . . . 7 73 5.3.1. Common settings . . . . . . . . . . . . . . . . . . . 7 74 5.3.2. High Latency . . . . . . . . . . . . . . . . . . . . 8 75 5.3.3. Unconstrained Low Latency . . . . . . . . . . . . . . 8 76 6. Automation . . . . . . . . . . . . . . . . . . . . . . . . . 8 77 6.1. Regression tests . . . . . . . . . . . . . . . . . . . . 9 78 6.2. Objective performance tests . . . . . . . . . . . . . . . 9 79 6.3. Periodic tests . . . . . . . . . . . . . . . . . . . . . 9 80 7. Informative References . . . . . . . . . . . . . . . . . . . 9 81 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 10 83 1. Introduction 85 When developing a video codec, changes and additions to the codec 86 need to be decided based on their performance tradeoffs. In 87 addition, measurements are needed to determine when the codec has met 88 its performance goals. This document specifies how the tests are to 89 be carried about to ensure valid comparisons and good decisions. 91 2. Subjective quality tests 93 Subjective testing is the preferable method of testing video codecs. 95 Because the IETF does not have testing resources of its own, it has 96 to rely on the resources of its participants. For this reason, even 97 if the group agrees that a particular test is important, if no one 98 volunteers to do it, or if volunteers do not complete it in a timely 99 fashion, then that test should be discarded. This ensures that only 100 important tests be done in particular, the tests that are important 101 to participants. 103 2.1. Still Image Pair Comparison 105 A simple way to determine superiority of one compressed image over 106 another is to visually compare two compressed images, and have the 107 viewer judge which one has a higher quality. This is mainly used for 108 rapid comparisons during development. For this test, the two 109 compressed images should have similar compressed file sizes, with one 110 image being no more than 5% larger than the other. In addition, at 111 least 5 different images should be compared. 113 2.2. Subjective viewing test 115 A subjective viewing test is the preferred method of evaluating the 116 quality. The subjective test should be performed as either 117 consecutively showing the video sequences on one screen or on two 118 screens located side-by-side. The testing procedure should normally 119 follow rules described in [BT500] and be performed with non-expert 120 test subjects. The result of the test could be (depending on the 121 test procedure) mean opinion scores (MOS) or differential mean 122 opinion scores (DMOS). Normally, confidence intervals are also 123 calculated to judge whether the difference between two encodings is 124 statistically significant. 126 2.3. Expert viewing 128 An expert viewing test can be performed in the case when an answer to 129 a particular question should be found. An example of such test can 130 be a test involving video coding experts on evaluation of a 131 particular problem, for example such as comparing the results of two 132 de-ringing filters. Depending on what information is sought, the 133 appropriate test procedure can be chosen. 135 3. Objective Metrics 137 Objective metrics are used in place of subjective metrics for easy 138 and repeatable experiments. Most objective metrics have been 139 designed to correlate with subjective scores. 141 The following descriptions give an overview of the operation of each 142 of the metrics. Because implementation details can sometimes vary, 143 the exact implementation is specified in C in the Daala tools 144 repository [DAALA-GIT]. 146 All of the metrics described in this document are to be applied to 147 the luma plane only. In addition, they are single frame metrics. 148 When applied to the video, the scores of each frame are averaged to 149 create the final score. 151 Codecs are allowed to internally use downsampling, but must include a 152 normative upsampler, so that the metrics run at the same resolution 153 as the source video. In addition, some metrics, such as PSNR and 154 FASTSSIM, have poor behavior on downsampled images, so it must be 155 noted in test results if downsampling is in effect. 157 3.1. Overall PSNR 159 PSNR is a traditional signal quality metric, measured in decibels. 160 It is directly drived from mean square error (MSE), or its square 161 root (RMSE). The formula used is: 163 20 * log10 ( MAX / RMSE ) 165 or, equivalently: 167 10 * log10 ( MAX^2 / MSE ) 169 where the error is computed over all the pixels in the video, which 170 is the method used in the dump_psnr.c reference implementation. 172 This metric may be applied to both the luma and chroma planes, with 173 all planes reported separately. 175 3.2. Frame-averaged PSNR 177 PSNR can also be calculated per-frame, and then the values averaged 178 together. This is reported in the same way as overall PSNR. 180 3.3. PSNR-HVS-M 182 The PSNR-HVS metric performs a DCT transform of 8x8 blocks of the 183 image, weights the coefficients, and then calculates the PSNR of 184 those coefficients. Several different sets of weights have been 185 considered. [PSNRHVS] The weights used by the dump_pnsrhvs.c tool in 186 the Daala repository have been found to be the best match to real MOS 187 scores. 189 3.4. SSIM 191 SSIM (Structural Similarity Image Metric) is a still image quality 192 metric introduced in 2004 [SSIM]. It computes a score for each 193 individual pixel, using a window of neighboring pixels. These scores 194 can then be averaged to produce a global score for the entire image. 195 The original paper produces scores ranging between 0 and 1. 197 For the metric to appear more linear on BD-rate curves, the score is 198 converted into a nonlinear decibel scale: 200 -10 * log10 (1 - SSIM) 202 3.5. Multi-Scale SSIM 204 Multi-Scale SSIM is SSIM extended to multiple window sizes [MSSSIM]. 206 3.6. Fast Multi-Scale SSIM 208 Fast MS-SSIM is a modified implementation of MS-SSIM which operates 209 on a limited number of scales and with modified weights [FASTSSIM]. 210 The final score is converted to decibels in the same manner as SSIM. 212 3.7. CIEDE2000 214 CIEDE2000 is a metric based on CIEDE color distances [CIEDE2000]. It 215 generates a single score taking into account all three chroma planes. 216 It does not take into consideration any structural similarity or 217 other psychovisual effects. 219 3.8. VMAF 221 Video Multi-method Assessment Fusion (VMAF) is a full-reference 222 perceptual video quality metric that aims to approximate human 223 perception of video quality [VMAF]. This metric is focused on 224 quality degradation due compression and rescaling. VMAF estimates 225 the perceived quality score by computing scores from multiple quality 226 assessment algorithms, and fusing them using a support vector machine 227 (SVM). Currently, three image fidelity metrics and one temporal 228 signal have been chosen as features to the SVM, namely Anti-noise SNR 229 (ANSNR), Detail Loss Measure (DLM), Visual Information Fidelity 230 (VIF), and the mean co-located pixel difference of a frame with 231 respect to the previous frame. 233 4. Comparing and Interpreting Results 235 4.1. Graphing 236 When displayed on a graph, bitrate is shown on the X axis, and the 237 quality metric is on the Y axis. For publication, the X axis should 238 be linear. The Y axis metric should be plotted in decibels. If the 239 quality metric does not natively report quality in decibels, it 240 should be converted as described in the previous section. 242 4.2. Bjontegaard 244 The Bjontegaard rate difference, also known as BD-rate, allows the 245 comparison of two different codecs based on a metric. This is 246 commonly done by fitting a curve to each set of data points on the 247 plot of bitrate versus metric score, and then computing the 248 difference in area between each of the curves. A cubic polynomial 249 fit is common, but will be overconstrained with more than four 250 samples. For higher accuracy, at least 10 samples and a cubic spline 251 fit should be used. In addition, if using a truncated BD-rate curve, 252 there should be at least 4 samples within the point of interest. 254 4.3. Ranges 256 The curve is split into three regions, for low, medium, and high 257 bitrate. The ranges are defined as follows: 259 o Low bitrate: 0.005 - 0.02 bpp 261 o Medium bitrate: 0.02 - 0.06 bpp 263 o High bitrate: 0.06 - 0.2 bpp 265 Bitrate can be calculated from bits per pixel (bpp) as follows: 267 bitrate = bpp * width * height * framerate 269 5. Test Sequences 271 5.1. Sources 273 Lossless test clips are preferred for most tests, because the 274 structure of compression artifacts in already-compressed clips may 275 introduce extra noise in the test results. However, a large amount 276 of content on the internet needs to be recompressed at least once, so 277 some sources of this nature are useful. The encoder should run at 278 the same bit depth as the original source. In addition, metrics need 279 to support operation at high bit depth. If one or more codecs in a 280 comparison do not support high bit depth, sources need to be 281 converted once before entering the encoder. 283 5.2. Test Sets 285 Sources are divided into several categories to test different 286 scenarios the codec will be required to operate in. For easier 287 comparison, all videos in each set should have the same color 288 subsampling, same resolution, and same number of frames. In 289 addition, all test videos must be publicly available for testing use, 290 to allow for reproducibility of results. All current test sets are 291 available for download [TESTSEQUENCES]. 293 o Still images are useful when comparing intra coding performance. 294 Xiph.org has four sets of lossless, one megapixel images that have 295 been converted into YUV 4:2:0 format. There are four sets that 296 can be used: 298 * subset1 (50 images) 300 * subset2 (50 images) 302 * subset3 (1000 images) 304 * subset4 (1000 images) 306 o video-hd-3, a set that consists of 1920x1080 clips from 307 [DERFVIDEO] (1500 frames total) 309 o vc-360p-1, a low quality video conferencing set (2700 frames 310 total) 312 o vc-720p-1, a high quality video conferencing set (2750 frames 313 total) 315 o netflix-4k-1, a cinematic 4K video test set (2280 frames total) 317 o netflix-2k-1, a 2K scaled version of netflix-4k-1 (2280 frames 318 total) 320 o twitch-1, a game sequence set (2280 frames total) 322 5.3. Operating Points 324 Two operating modes are defined. High latency is intended for on 325 demand streaming, one-to-many live streaming, and stored video. Low 326 latency is intended for videoconferencing and remote access. 328 5.3.1. Common settings 329 Encoders should be configured to their best settings when being 330 compared against each other: 332 o vp10: -codec=vp10 -ivf -frame-parallel=0 -tile-columns=0 -cpu- 333 used=0 -threads=1 335 5.3.2. High Latency 337 The encoder should be run at the best quality mode available, using 338 the mode that will provide the best quality per bitrate (VBR or 339 constant quality mode). Lookahead and/or two-pass are allowed, if 340 supported. One parameter is provided to adjust bitrate, but the 341 units are arbitrary. Example configurations follow: 343 o x264: -crf=x 345 o x265: -crf=x 347 o daala: -v=x -b 2 349 o vp10: -end-usage=q -cq-level=x -lag-in-frames=25 -auto-alt-ref=2 351 5.3.3. Unconstrained Low Latency 353 The encoder should be run at the best quality mode available, using 354 the mode that will provide the best quality per bitrate (VBR or 355 constant quality mode), but no frame delay, buffering, or lookahead 356 is allowed. One parameter is provided to adjust bitrate, but the 357 units are arbitrary. Example configurations follow: 359 o x264: -crf-x -tune zerolatency 361 o x265: -crf=x -tune zerolatency 363 o daala: -v=x 365 o vp10: -end-usage=q -cq-level=x -lag-in-frames=0 367 6. Automation 369 Frequent objective comparisons are extremely beneficial while 370 developing a new codec. Several tools exist in order to automate the 371 process of objective comparisons. The Compare-Codecs tool allows BD- 372 rate curves to be generated for a wide variety of codecs 373 [COMPARECODECS]. The Daala source repository contains a set of 374 scripts that can be used to automate the various metrics used. In 375 addition, these scripts can be run automatically utilizing 376 distributed computers for fast results, with the AreWeCompressedYet 377 tool [AWCY]. Because of computational constraints, several levels of 378 testing are specified. 380 6.1. Regression tests 382 Regression tests run on a small number of short sequences. The 383 regression tests should include a number of various test conditions. 384 The purpose of regression tests is to ensure bug fixes (and similar 385 patches) do not negatively affect the performance. 387 6.2. Objective performance tests 389 Changes that are expected to affect the quality of encode or 390 bitstream should run an objective performance test. The performance 391 tests should be run on a wider number of sequences. If the option 392 for the objective performance test is chosen, wide range and full 393 length simulations are run on the site and the results (including all 394 the objective metrics) are generated. 396 6.3. Periodic tests 398 Periodic tests are run on a wide range of bitrates in order to gauge 399 progress over time, as well as detect potential regressions missed by 400 other tests. 402 7. Informative References 404 [AWCY] Xiph.Org, "Are We Compressed Yet?", 2015, . 407 [BT500] ITU-R, "Recommendation ITU-R BT.500-13", 2012, . 411 [CIEDE2000] 412 Yang, Y., Ming, J., and N. Yu, "Color Image Quality 413 Assessment Based on CIEDE2000", 2012, 414 . 416 [COMPARECODECS] 417 Alvestrand, H., "Compare Codecs", 2015, 418 . 420 [DAALA-GIT] 421 Xiph.Org, "Daala Git Repository", 2015, 422 . 424 [DERFVIDEO] 425 Terriberry, T., "Xiph.org Video Test Media", n.d., . 428 [FASTSSIM] 429 Chen, M. and A. Bovik, "Fast structural similarity index 430 algorithm", 2010, . 433 [L1100] Bossen, F., "Common test conditions and software reference 434 configurations", JCTVC L1100, 2013, 435 . 437 [MSSSIM] Wang, Z., Simoncelli, E., and A. Bovik, "Multi-Scale 438 Structural Similarity for Image Quality Assessment", n.d., 439 . 441 [PSNRHVS] Egiazarian, K., Astola, J., Ponomarenko, N., Lukin, V., 442 Battisti, F., and M. Carli, "A New Full-Reference Quality 443 Metrics Based on HVS", 2002. 445 [SSIM] Wang, Z., Bovik, A., Sheikh, H., and E. Simoncelli, "Image 446 Quality Assessment: From Error Visibility to Structural 447 Similarity", 2004, 448 . 450 [STEAM] Valve Corporation, "Steam Hardware & Software Survey: June 451 2015", June 2015, 452 . 454 [TESTSEQUENCES] 455 Daede, T., "Test Sets", n.d., . 458 [VMAF] Aaron, A., Li, Z., Manohara, M., Lin, J., Wu, E., and C. 459 Kuo, "Challenges in cloud based ingest and encoding for 460 high quality streaming media", 2015, . 463 Authors' Addresses 465 Thomas Daede 466 Mozilla 468 Email: tdaede@mozilla.com 469 Andrey Norkin 470 Netflix 472 Email: anorkin@netflix.com