Network Working Group T. Davies Internet-Draft Cisco Intended status: Standards Track March 16, 2016 Expires: September 17, 2016 Quantisation matrices for Thor video coding draft-davies-netvc-qmtx-00 Abstract This draft describes a family of default quantisation matrices that may be used to improve perceptual quality when encoding with Thor. Similar quantisation matrix designs may be used in most block-based video and image codecs. 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 17, 2016. Copyright Notice Copyright (c) 2016 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 the Trust Legal Provisions and are provided without warranty as described in the Simplified BSD License. Davies Expires September 17, 2016 [Page 1]

Internet-Draft QMTX March 2016 Table of Contents 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2 2. Definitions . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.1. Terminology . . . . . . . . . . . . . . . . . . . . . . . 2 3. Quantisation matrix design . . . . . . . . . . . . . . . . . 3 3.1. The function of quantisation matrices . . . . . . . . . . 3 3.2. Quantisation matrices in AVC and HEVC . . . . . . . . . . 4 3.3. Quantisation matrices in Thor . . . . . . . . . . . . . . 4 3.4. Implementation . . . . . . . . . . . . . . . . . . . . . 5 4. Compression performance . . . . . . . . . . . . . . . . . . . 6 5. Informative References . . . . . . . . . . . . . . . . . . . . 7 Author's Address . . . . . . . . . . . . . . . . . . . . . . . . 7 1. Introduction This document describes a family of default quantisation matrices that may be used to improve perceptual quality when encoding with Thor. The quantisation matrices are designed to be near-flat at high quantisation levels and more strongly profiled at low quantisation levels, to avoid ringing artefacts and better shape quantisation error across a whole sequence with varying quantisation levels. 2. Definitions 2.1. Terminology This document uses the following terms. QP: quantisation parameter QM: quantisation matrix CSF: contrast sensitivity function BDR: Bjontegaard Delta-Rate Davies Expires September 17, 2016 [Page 2]

Internet-Draft QMTX March 2016 3. Quantisation matrix design 3.1. The function of quantisation matrices Quantisation matrices work by shaping the residual error after quantisation in the spatial frequency domain, usually the DCT domain. This is done by varying the quantisation factor applied across spatial frequencies in the transform block. Typically a high quantisation factor is applied at high spatial frequencies and a low one at low spatial frequencies. The aim is roughly to match a Contrast Sensitivity Function for the human visual system. This provides a curve of sensitivity to detail (and therefore coding errors) with spatial frequency. Given known resolutions and assumed viewing distances, a weighting function can be simply defined for all the coefficients in a transform block. This simple approach is complicated, however, by a number of factors. The first is the CSF is in reality not a simple function of spatial frequency, but depends on factors such as brightness which are imperfectly corrected for by television gammas. There is little that can be done about that in the quantisation matrices themselves, but adjusting QP itself may help. The second factor is that CSFs are determined experimentally based on models of Just Noticeable Difference (JND) and do not reflect so well the impact of distortions well above this level. Adjustments at high levels of quantisation are needed to reflect this. Finally, applying quantisation matrices to video is affected by the fact that most frames are predicted and the QM is applied to the residual after prediction. This means that the quantisation error for a block consists of the quantisation error in the reference block, plus any additional error introduced in the current block. These errors will add if they are uncorrelated, but they may well be correlated at high QP. Despite these difficulties, QMs are widely used and known to work well, and are available in video coding standards such as H264/AVC and H265/HEVC [AVC,HEVC]. Davies Expires September 17, 2016 [Page 3]

Internet-Draft QMTX March 2016 3.2 Quantisation matrix design in AVC and HEVC Quantisation matrices are available in a number of different codecs. The design in AVC and HEVC is to provide default matrices together with the ability to signal bespoke matrices [AVC,HEVC]. These matrices must cover all the different transform block sizes, components (Y, Cb, Cr) and intra and inter frame or block types, with fall-backs defined if bespoke matrices are not provided. Default inter block matrices are flatter than intra matrices, no doubt because of the noise-addition effect described in section 3.1: if they had the same profile as for intra then the overall profile of the combined prediction + residual could be over-shaped. 3.3 Design of quantisation matrices in Thor Thor provides a set of matrices for each component of 420-sampled video, for each block size and each quantisation parameter. The principles behind the design are as follows: 1) QP dependence. Matrices become flatter as quantisation levels increase 2) Energy preservation for intra. The inverse quantisation matrices for intra blocks are normalised to approximately preserve energy of the residual 3) DC preservation for inter. The inverse quantisation matrices for inter blocks are normalised to preserve the DC level 4) Matrices are also flatter for inter blocks than for intra blocks. 5) Quantisation matrix strength is globally adjustable The QP dependence takes account of a number of factors. Firstly it reflects that inter blocks typically have higher QPs than the blocks used to predict them. This means that flattening the matrices at higher QP naturally prevents over-shaping the quantisation error. Secondly, the high-QP flattening process also reflects the fact that errors at this level are very visible even at high spatial frequencies. Strong error-shaping at these QP levels leads to very visible additional ringiness. SSIM-based metrics [SSIM,MSSSIM,FASTSSIM] indicate that preserving image variances and therefore residual energies is perceptually important. This is feasible for intra where residuals are substantial but in the case of inter it is also important to preserve DC levels since getting these wrong can produce very visible artefacts. Davies Expires September 17, 2016 [Page 4]

Internet-Draft QMTX March 2016 Intra frames tend to have lower QP than inter frames, and this means that QP dependence absorbs most of the requirement for inter matrices to be flatter than intra matrices. However inter matrices are still a little flatter, to take account of the different characteristics of intra and inter blocks within the same frame. In determining the quantisation matrix, there are 12 possible sets available giving a new set of matrix for each change of approximately 4 in quantisation value. Thor also supports a global adjustment or strength parameter, which offsets the LUT mapping quantisation parameter to quantisation matrix set. This is a value from -32 to 31. A value of -32 will reduce the qp used by 32, increasing the strength of quantisation matrix dramatically. Likewise a value of 31 will eliminate quantisation matrices for all but the smallest QPs. The effect of the ability to signal strength, and the provision of a range of QP-dependent matrices are intended to remove the need to signal bespoke matrices at all. 3.4 Implementation Quantisation matrices are applied as multiplicative factors in forward or inverse quantisation processes. In Thor the basic unweighted dequantisation process for a coefficient c with quantisation parameter q is based on two values: scale[q], which depends only on q%6, and shift[q] which depends only on q/6, the block size and the signal dynamic range. scale[q] takes care of quantisation step sizes which fall between powers of 2 and shift[q] takes care of the basic power of 2 part of the quantisation step. The formula for unweighted dequantisation is then: c -> (c*scale[q] + (1<<(shift[q]-1))) >> shift[q] (1) for positive shift[q], otherwise c -> (c*scale[q])<<(-shift[q]) (2) To apply a matrix M to a coefficient c[i,j] at position (i,j) within a block, the formulae (1), (2) change to: c[i,j]->(c[i,j]*M[i,j]*scale[q]+(1<<(shift[q]+5)))>>(shift[q]+6) (3) if shift[q]+6 > 0, otherwise c[i,j]->(c[i,j]*M[i,j]*scale[q])<<(-shift[q]-6) (4) otherwise. Davies Expires September 17, 2016 [Page 5]

Internet-Draft QMTX March 2016 Exactly complementary formulae can be derived for the forward quantisation process. 4. Compression performance Although largely a visual tool, the effectiveness of QMs can be inferred by changes to PSNRHVS [PSNRHVS] and FASTSSIM metrics. FASTSSIM tends to over-estimate gains a little, as it has a bias towards low-pass filtering. Overall BDR results for the Low-Delay B (LDB) and High-Delay B GOP 16 configuration (HDB16) are as follows (QPs 22, 27, 32, 37): Config | PSNR | PSNRHVS | FASTSSIM | -------------------------------------------- LDB | +1.1% | -3.3% | -9.0% | -------------------------------------------- HDB | +2.2% | -2.6% | -11.6% | -------------------------------------------- These were computed on the same test sequences as in IRFVC. FASTSSIM and PSNRHVS gains are typically larger, and PSNR losses smaller, for higher resolution material. Davies Expires September 17, 2016 [Page 6]

Internet-Draft QMTX March 2016 5. Informative References [AVC] ITU-T Recommendation H.264, "Advanced video coding for generic audiovisual services", March 2010. [HEVC] ITU-T Recommendation H.265, "High efficiency video coding", April 2013. [FASTSSIM] Chen, M. and A. Bovik, "Fast structural similarity index algorithm", 2010, <http://live.ece.utexas.edu/publications /2011/chen_rtip_2011.pdf>. [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. [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>. [IRFVC] Davies, T. "Interpolated reference frames for video coding", IETF draft https://www.ietf.org/id/draft-davies-netvc-irfvc-00.txt Author's Address: Thomas Davies Cisco Feltham UK Email: thdavies@cisco.com Davies Expires September 17, 2016 [Page 7]

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