Data-Driven Modules for Objective Visual Quality Assessment Focusing on Benchmarking and SLAs

Barri, Adriaan; Dooms, Ann

doi:10.1109/jstsp.2016.2637164

Cited by 4 publications

(2 citation statements)

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“…It is challenging to distinguish features representing impairments from features that are part of the source content, and this is why NR metrics in general are not capable of predicting subjective video quality as accurately as state-of-the-art FR metrics. It has been shown that different distortion specific metrics (e.g., blur, blockiness and jerkiness) do not work well alone for NR assessment of global video quality, but they can be combined into a more accurate generic metric by machine learning [40]- [42]. Unfortunately, video quality metrics based on machine learning tend to be prone to irreproducibility, due to overfitting and the fact that the commonly used machine learning algorithms do not include a mechanism to ensure that different features are combined in a consistent manner [42].…”

Section: Objective Assessment Of Packet Loss Artifactsmentioning

confidence: 99%

“…It has been shown that different distortion specific metrics (e.g., blur, blockiness and jerkiness) do not work well alone for NR assessment of global video quality, but they can be combined into a more accurate generic metric by machine learning [40]- [42]. Unfortunately, video quality metrics based on machine learning tend to be prone to irreproducibility, due to overfitting and the fact that the commonly used machine learning algorithms do not include a mechanism to ensure that different features are combined in a consistent manner [42]. To make sure that a learning-based quality metric considers packet loss artifacts properly, the metric has to be trained using video sequences with similar artifacts.…”

Section: Objective Assessment Of Packet Loss Artifactsmentioning

confidence: 99%

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Study of the Subjective Visibility of Packet Loss Artifacts in Decoded Video Sequences

Korhonen

2018

IEEE Trans. on Broadcast.

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Packet loss is a significant cause of visual impairments in video broadcasting over packet-switched networks. There are several subjective and objective video quality assessment methods focused on the overall perception of video quality. However, less attention has been paid on the visibility of packet loss artifacts appearing in spatially and temporally limited regions of a video sequence. In this paper, we present the results of a subjective study, using a methodology where a video sequence is displayed on a touchscreen and the users tap it in the positions where they observe artifacts. We also analyze the objective features derived from those artifacts, and propose different models for combining those features into an objective metric for assessing the noticeability of the artifacts. The practical results show that the proposed metric predicts visibility of packet loss impairments with a reasonable accuracy. The proposed method can be applied for developing packetization and error recovery schemes to minimize the subjectively experienced distortion in error-prone networked video systems.

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Section: Objective Assessment Of Packet Loss Artifactsmentioning

confidence: 99%