No abstract

The trade-offs between compression performance and encoding complexity are key in software video encoding, even more so with increasing pressure on sustainability. Previous work "Towards much better SVT-AV1 quality-cycles tradeoffs for VOD applications" [1] described three approaches of evaluating compression efficiency vs cycles trade-offs within a convex-hull framework using the Dynamic Optimizer (DO) algorithm developed in [2] [3] for VOD applications.In parallel, the new video codec enhancer LCEVC (Low Complexity Enhancement Video Coding) [4], designed to provide gains in speed-quality trade-offs, has recently been standardized as MPEG-5 Part 2. The core idea of LCEVC is to use any video coding standard (such as AV1) as a base encoder at a lower resolution, and then reduce artifacts and reconstruct a full resolution output by combining the decoded low-resolution output with up to two low-complexity reconstruction enhancement sub-layers of the residual data. This paper starts by applying LCEVC to SVT-AV1 [5], as well as x264 [6] and x265 [7], while using two of the approaches presented in [1] to evaluate the resulting compression efficiency vs cycles trade-offs. The paper then discusses the benefits of LCEVC towards higher playback speed and lower battery power consumption when using AV1 software decoding.Results show that, with fast-encoding parameter selection using the discrete convex hull methodology, LCEVC improves the quality-cycles trade-offs for all the tested codecs and across the full complexity range. In the case of SVT-AV1, LCEVC yields a ~40% reduction in computations while achieving the same quality levels according to VMAF_NEG [8]. LCEVC also enlarges the set of mobile devices capable of playing HD as well as high-frame-rate content encoded with AV1 and extends mobile battery life by up to 50% with respect to state-of-the-art AV1 software decoding.

Fusion-based quality assessment has emerged as a powerful method for developing high-performance quality models from quality models that individually achieve lower performances. A prominent example of such an algorithm is VMAF, which has been widely adopted as an industry standard for video quality prediction along with SSIM. In addition to advancing the state-of-the-art, it is imperative to alleviate the computational burden presented by the use of a heterogeneous set of quality models. In this paper, we unify "atom" quality models by computing them on a common transform domain that accounts for the Human Visual System, and we propose FUNQUE, a quality model that fuses unified quality evaluators. We demonstrate that in comparison to the stateof-the-art, FUNQUE offers significant improvements in both correlation against subjective scores and efficiency, due to computation sharing.

iSIZE UK(a) PSNR (b) SSIM (c) VMAF-NEG (d) VMAF (e) Proposed P.910-MOS Figure 1: BD-rate (Bjontegaard Delta-rate) vs. runtime of video encoders when assessed in terms of: PSNR, SSIM, VMAF-NEG, VMAF and the P.910-MOS fused metric derived by our proposal. The utilized encoders (x264 AVC, vpxenc VP9, and svt-av1 AV1, with and without preprocessing) lead to different BD-rate results for each metric. Instead of ad-hoc averaging of BD-rates, we propose to consolidate this difference via domain-specific video quality metric fusion with limited subjective testing.