A new objective quality metric for phase hologram processing

In multimedia transmission, it is important to rely on an objective quality metric which accurately represents the subjective quality of processed images and video sequences. Reduced-reference metrics make use of side-information that is transmitted to the receiver for estimating the quality of the received sequence with low complexity. In this paper, an Edge-based Dissimilarity Reduced-Reference video quality metric with low overhead bitrate is proposed. The metric is evaluated by finding the dissimilarity between the edge information of original and distorted sequences. The edge degradation can be detected in this manner as perceived video quality is highly associated with edge structural. Due to the high overhead using the Soergel distance, it is pertinent to find a way to reduce the overhead while maintaining the edge information that can convey the quality measure of the sequences. The effects of different edge detection operator, video resolution and file compressor are investigated. The aim of this paper is to significantly reduce the bitrate required in order to transmit the side information overhead as the reduced reference video quality metric. From the results obtained, the side information extracted using Sobel edge detector maintained consistency throughout the reduction of spatial and temporal down-sample.

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A Color-Based Objective Quality Metric for Point Cloud Contents

2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX) ◽

10.1109/qomex48832.2020.9123089 ◽

2020 ◽

Cited By ~ 3

Author(s):

Irene Viola ◽

Shishir Subramanyam ◽

Pablo Cesar

Keyword(s):

Point Cloud ◽

Quality Metric ◽

Objective Quality

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AMBIQUAL: Towards a Quality Metric for Headphone Rendered Compressed Ambisonic Spatial Audio

Applied Sciences ◽

10.3390/app10093188 ◽

2020 ◽

Vol 10 (9) ◽

pp. 3188

Author(s):

Miroslaw Narbutt ◽

Jan Skoglund ◽

Andrew Allen ◽

Michael Chinen ◽

Dan Barry ◽

...

Keyword(s):

Service Providers ◽

Spatial Audio ◽

Localization Accuracy ◽

Quality Metric ◽

Audio Quality ◽

Listening Tests ◽

Objective Quality ◽

Streaming Service ◽

Quality Assessments

Spatial audio is essential for creating a sense of immersion in virtual environments. Efficient encoding methods are required to deliver spatial audio over networks without compromising Quality of Service (QoS). Streaming service providers such as YouTube typically transcode content into various bit rates and need a perceptually relevant audio quality metric to monitor users’ perceived quality and spatial localization accuracy. The aim of the paper is two-fold. First, it is to investigate the effect of Opus codec compression on the quality of spatial audio as perceived by listeners using subjective listening tests. Secondly, it is to introduce AMBIQUAL, a full reference objective metric for spatial audio quality, which derives both listening quality and localization accuracy metrics directly from the B-format Ambisonic audio. We compare AMBIQUAL quality predictions with subjective quality assessments across a variety of audio samples which have been compressed using the Opus 1.2 codec at various bit rates. Listening quality and localization accuracy of first and third-order Ambisonics were evaluated. Several fixed and dynamic audio sources (single and multiple) were used to evaluate localization accuracy. Results show good correlation regarding listening quality and localization accuracy between objective quality scores using AMBIQUAL and subjective scores obtained during listening tests.

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Joint Geometry and Color Projection-based Point Cloud Quality Metric

10.36227/techrxiv.15112383.v1 ◽

2021 ◽

Author(s):

Alireza Javaheri ◽

Catarina Brites ◽

Fernando Pereira ◽

Joao Ascenso

Keyword(s):

Point Cloud ◽

Pearson Correlation ◽

Quality Metrics ◽

Experimental Results ◽

Reliable Estimate ◽

Quality Metric ◽

Joint Geometry ◽

Objective Quality ◽

Correlation Performance

Point cloud coding solutions have been recently standardized to address the needs of multiple application scenarios. The design and assessment of point cloud coding methods require reliable objective quality metrics to evaluate the level of degradation introduced by compression or any other type of processing. Several point cloud objective quality metrics has been recently proposed to reliable estimate human perceived quality, including the so-called projection-based metrics. In this context, this paper proposes a joint geometry and color projection-based point cloud objective quality metric which solves the critical weakness of this type of quality metrics, i.e., the misalignment between the reference and degraded projected images. Moreover, the proposed point cloud quality metric exploits the best performing 2D quality metrics in the literature to assess the quality of the projected images. The experimental results show that the proposed projection-based quality metric offers the best subjective-objective correlation performance in comparison with other metrics in the literature. The Pearson correlation gains regarding D1-PSNR and D2-PSNR metrics are 17% and 14.2 when data with all coding degradations is considered.

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