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Towards Better Quality Assessment of High-Quality Videos Towards Better Quality Assessment of High-Quality Videos Suiyi Ling Yoann Baveye Deepthi Nandakumar [email protected] [email protected] [email protected] CAPACITÉS SAS CAPACITÉS SAS Amazon Video Nantes, France Nantes, France Bangalore, India Sriram Sethuraman Patrick Le Callet [email protected] [email protected] Amazon Video LS2N lab, University of Nantes Bangalore, India Nantes, France ABSTRACT significantly along with the rapid development of display manu- In recent times, video content encoded at High-Definition (HD) and facturers, high-quality video producers (film industry) and video Ultra-High-Definition (UHD) resolution dominates internet traf- streaming services. Video quality assessment (VQA) metrics are fic. The significantly increased data rate and growing expectations aimed at predicting the perceived quality, ideally, by mimicking of video quality from users create great challenges in video com- the human visual system. Such metrics are imperative for achiev- pression and quality assessment, especially for higher-resolution, ing higher compression and for monitoring the delivered video higher-quality content. The development of robust video quality quality. According to one of the most recent relevant studies [21], assessment metrics relies on the collection of subjective ground correlation between the subjective scores and the objective scores truths. As high-quality video content is more ambiguous and diffi- predicted by commonly used video quality metrics is significantly cult for a human observer to rate, a more distinguishable subjective poorer in the high quality range (HD) than the ones in the lower protocol/methodology should be considered. In this study, towards quality range (SD). Thus, a reliable VQA metric is highly desirable better quality assessment of high-quality videos, a subjective study for the high quality range to tailor the compression level to meet was conducted focusing on high-quality HD and UHD content with the growing user expectation. the Degradation Category Rating (DCR) protocol. Commonly used The robustness of objective VQA metrics is grounded in subjec- video quality metrics were benchmarked in two quality ranges. tive experiments, where ground-truth quality scores are collected from human observers. The quality (e.g., whether there are noisy CCS CONCEPTS ratings) and the distinguishability (e.g. whether there are enough significant pairs) of the collected subjective data is crucial forthe • Human-centered computing; • Applied computing; eventual development of quality metrics [14, 15]. It is emphasized in [21] that the subjective scores obtained with Absolute Category KEYWORDS Rating (ACR) protocol do not consistently distinguish between qual- datasets, HD, UHD, video quality assessment ity levels in the high-quality region, even when significant quality ACM Reference Format: differences were expected. In order to develop quality metrics with Suiyi Ling, Yoann Baveye, Deepthi Nandakumar, Sriram Sethuraman, and Patrick better distinguishability in high quality range, a more discrimina- Le Callet. 2020. Towards Better Quality Assessment of High-Quality Videos. tive experimental protocol should be considered when collecting In 1st Workshop on Quality of Experience (QoE) in Visual Multimedia Appli- subjective data. cations (QoEVMA’20), October 16, 2020, Seattle, WA, USA. ACM, New York, Based on the discussion above, towards better quantification of NY, USA, 7 pages. https://doi.org/10.1145/3423328.3423496 the perceived quality of high-quality videos, we conducted subjec- tive studies on high-quality HD and UHD contents. To enhance the 1 INTRODUCTION distinguishability of the obtained subjective data in the high-quality range, the Degradation Category Rating (DCR) methodology was Nowadays, video streaming, especially the High-Definition and utilized. When benchmarking the objective quality metrics in the Ultra-High-Definition video contents, accounts for the maximal low and high quality range, in addition to the different types of percentage of Internet downstream traffic [26]. At the same time, correlation against the subjective metrics, the “Krasula” framework the expectation of Quality-of-Experience (QoE) of users grows is employed to compare the area-under-the-curve (AUC) for the "different-or-similar" and "better-or-worse" discrimination tasks. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM 2 RELATED WORK must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a In the last decades, many subjective studies were conducted for fee. Request permissions from [email protected]. better quality assessment of UHD contents. One of the very first QoEVMA’20, October 16, 2020, Seattle, WA, USA subjective test considering UHD contents was presented in [2] for © 2020 Association for Computing Machinery. ACM ISBN 978-1-4503-8158-1/20/10...$15.00 the investigation of using high efficiency video coding (HEVC) for https://doi.org/10.1145/3423328.3423496 4K-UHD TV broadcasting services. Yet, this study is limited as only three bitrates were tested. The study summarized in [3] compared computation of TI is based on the difference between the the perceived quality of HD and UHD at the same bitrate, encoded pixel values (of the luminance plane) of successive frames. with HEVC. Another subjective experiment for 4k UHD content As detailed in [8], to get a global value for the whole video, was shown in [27], but only 6 contents were considered. Recently, in the standard deviation over space (of the values of the pixels order to benchmark the performances of the popular and emerging of the difference frames) is computed. The maximum value coding techniques, e.g., the H.264 Advanced Video Coding (AVC), over time is then calculated to get the final TI score for the HEVC, VP9, Audio Video Coding Standard (AVS2) and AOMedia clip. In this study, we also consider calculating the minimum Video 1 (AV1), a large scale subjective 4k dataset was collected. and mean values. However, these studies did not lay specific emphasis to the high- (3) Colorfulness (3 dim): This is an important visual feature quality range. In [21], a more recent study was conducted to better having a significant impact on the perceptual quality ofa measure and optimize video quality for HD, especially for high scene. Recently, in [1], the authors showed the performance quality ranges. Nevertheless, in this study (1) the quality range was of the state-of-the-art colorfulness metrics with the help divided based on the encoding resolution, i.e., the frame sizes, instead of subjective experiment data. From this study, the metric of using the Mean Opinion Scores (MOS); (2) UHD contents were proposed by Hasler et. al. [6] was selected, since it overcomes not taken considered; (3) only the correlation coefficients between the others. Analogous temporal pooling methods are utilized subjective scores and predicted objective scores were considered after obtaining the colorfulness score per frame. when benchmarking the quality metrics, which does not lend itself (4) Textural features (12 dim): Contrast is one of the most im- to a deeper analysis; (4) Absolute Category Rating was employed portant textural indicators, strongly related to the physio- for the subjective ratings, while more discriminating subjective logical procedure of perceiving image quality. In addition protocols need to be considered as discussed earlier. to this, many textural features [11, 19], such as entropy, ho- mogeneity and correlation (of a pixel to its neighbor over 3 SUBJECTIVE EXPERIMENT SETUP the whole image) have been used to characterize the im- age. These textural features can be extracted directly by 3.1 Content selection exploiting Gray Level Co-occurrence Matrix (GLCM) [5]. Content selection is a crucial step in a test design [24]. The contents More specifically, in this study, the contrast, entropy, homo- used in this study were selected from a candidate set that contains geneity and correlation were computed using the GLCM, 229 uncompressed 1 minute-long videos from a top streaming ser- and the corresponding extracted features are denoted as vice provider. These videos had different source resolutions (from GLCM-Contrast, GLCM-Entropy, GLCM-Homogeneity, and UHD to 640x480) and frame-rates. From these clips, 10-seconds GLCM-Correlation respectively. After computing the four long single-scene UHD and HD videos were selected. texture features frame-wise, similarly, minimum, maximum In a nutshell, to select representative source videos that cover a and mean values are computed across all the frames. wide-range of content characteristics and ambiguity behaviors, we first select representative features based on their correlation with 3.1.2 Feature Selection based on Content-Ambiguity . As pointed the ambiguity level of the contents. Contents are then clustered out in [17], some of the contents tend to be more complex for ob- using the selected features so that sequences belonging to the same servers to judge confidently. For instance, a content with numerous cluster have similar characteristics. With the representative clus-
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