Offset distortion traces for trace-based evaluation of video quality after network transport

Objective Video Quality Method Based on Mutual Information and Human Visual SystemIn this paper we present the objective video quality metric based on mutual information and Human Visual System. The calculation of proposed metric consists of two stages. In the first stage of quality evaluation whole original and test sequence are pre-processed by the Human Visual System. In the second stage we calculate mutual information which has been utilized as the quality evaluation criteria. The mutual information was calculated between the frame from original sequence and the corresponding frame from test sequence. For this testing purpose we choose Foreman video at CIF resolution. To prove reliability of our metric were compared it with some commonly used objective methods for measuring the video quality. The results show that presented objective video quality metric based on mutual information and Human Visual System provides relevant results in comparison with results of other objective methods so it is suitable candidate for measuring the video quality.

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The Influence of Video Quality on Sponsorship Effects

Korean Journal of Sport Management ◽

10.31308/kssm.23.2.7 ◽

2018 ◽

Vol 23 (2) ◽

pp. 97-114

Author(s):

Sanghak Lee ◽

Paul M Pedersen

Keyword(s):

Video Quality

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Combining Video Quality Metrics To Select Perceptually Accurate Resolution In A Wide Quality Range: A Case Study

10.1109/icip42928.2021.9506310 ◽

2021 ◽

Author(s):

Madhukar Bhat ◽

Jean-Marc Thiesse ◽

Patrick Le Callet

Keyword(s):

Video Quality ◽

Quality Metrics ◽

Video Quality Metrics

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Fuzzy-Based Quality Adaptation Algorithm for Improving QoE from MPEG-DASH Video

Applied Sciences ◽

10.3390/app11115270 ◽

2021 ◽

Vol 11 (11) ◽

pp. 5270

Author(s):

Waqas ur Rahman ◽

Md Delowar Hossain ◽

Eui-Nam Huh

Keyword(s):

State Of The Art ◽

Video Quality ◽

Buffer Size ◽

Bandwidth Utilization ◽

Adaptation Algorithm ◽

Control Laws ◽

Quality Video ◽

Video Player ◽

High Quality Video ◽

Abr Algorithm

Video clients employ HTTP-based adaptive bitrate (ABR) algorithms to optimize users’ quality of experience (QoE). ABR algorithms adopt video quality based on the network conditions during playback. The existing state-of-the-art ABR algorithms ignore the fact that video streaming services deploy segment durations differently in different services, and HTTP clients offer distinct buffer sizes. The existing ABR algorithms use fixed control laws and are designed with predefined client/server settings. As a result, adaptation algorithms fail to achieve optimal performance across a variety of video client settings and QoE objectives. We propose a buffer- and segment-aware fuzzy-based ABR algorithm that selects video rates for future video segments based on segment duration and the client’s buffer size in addition to throughput and playback buffer level. We demonstrate that the proposed algorithm guarantees high QoE across various video player settings and video content characteristics. The proposed algorithm efficiently utilizes bandwidth in order to download high-quality video segments and to guarantee high QoE. The results from our experiments reveal that the proposed adaptation algorithm outperforms state-of-the-art algorithms, providing improvements in average video rate, QoE, and bandwidth utilization, respectively, of 5% to 18%, about 13% to 30%, and up to 45%.

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