scholarly journals A Color-Based Objective Quality Metric for Point Cloud Contents

2020 ◽  
Author(s):  
Irene Viola ◽  
Shishir Subramanyam ◽  
Pablo Cesar
Keyword(s):  
Point Cloud ◽  
Quality Metric ◽  
Objective Quality

scholarly journals Joint Geometry and Color Projection-based Point Cloud Quality Metric

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.

scholarly journals Joint Geometry and Color Projection-based Point Cloud Quality Metric

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.

scholarly journals Study of Subjective and Objective Quality Evaluation of 3D Point Cloud Data by the JPEG Committee

2019 ◽  
Vol 2019 (10) ◽  
pp. 312-1-312-7 ◽  
Author(s):  
Stuart Perry ◽  
António Pinheiro ◽  
Emil Dumic ◽  
Luis A da Silva Cruz
Keyword(s):  
Point Cloud ◽  
Quality Evaluation ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Objective Quality

scholarly journals A comprehensive study of the rate-distortion performance in MPEG point cloud compression

2019 ◽  
Vol 8 ◽  
Author(s):  
Evangelos Alexiou ◽  
Irene Viola ◽  
Tomás M. Borges ◽  
Tiago A. Fonseca ◽  
Ricardo L. de Queiroz ◽  
...  
Keyword(s):  
Point Cloud ◽  
State Of The Art ◽  
Subjective Evaluation ◽  
Rate Distortion ◽  
Point Clouds ◽  
Current Approach ◽  
User Engagement ◽  
Viable Solution ◽  
Wide Range ◽  
Objective Quality

Abstract Recent trends in multimedia technologies indicate the need for richer imaging modalities to increase user engagement with the content. Among other alternatives, point clouds denote a viable solution that offers an immersive content representation, as witnessed by current activities in JPEG and MPEG standardization committees. As a result of such efforts, MPEG is at the final stages of drafting an emerging standard for point cloud compression, which we consider as the state-of-the-art. In this study, the entire set of encoders that have been developed in the MPEG committee are assessed through an extensive and rigorous analysis of quality. We initially focus on the assessment of encoding configurations that have been defined by experts in MPEG for their core experiments. Then, two additional experiments are designed and carried to address some of the identified limitations of current approach. As part of the study, state-of-the-art objective quality metrics are benchmarked to assess their capability to predict visual quality of point clouds under a wide range of radically different compression artifacts. To carry the subjective evaluation experiments, a web-based renderer is developed and described. The subjective and objective quality scores along with the rendering software are made publicly available, to facilitate and promote research on the field.

scholarly journals Edge Dissimilarity Reduced-Reference Quality Metric with Low Overhead Bitrate

2018 ◽  
Vol 10 (2) ◽  
pp. 631
Author(s):  
Farah Diyana Abdul Rahman ◽  
Dimitris Agrafiotis ◽  
Ahmad Imran Ibrahim
Keyword(s):  
Side Information ◽  
Video Quality ◽  
Low Complexity ◽  
Edge Information ◽  
Quality Metric ◽  
Objective Quality ◽  
Reference Quality ◽  
Edge Based ◽  
Video Quality Metric

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.

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

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.

A novel point cloud quality assessment metric based on perceptual color distance patterns

2021 ◽  
Vol 2021 (9) ◽  
pp. 256-1-256-11
Author(s):  
Rafael Diniz ◽  
Pedro Garcia Freitas ◽  
Mylène Farias
Keyword(s):  
Quality Assessment ◽  
Point Cloud ◽  
State Of The Art ◽  
Perceptual Quality ◽  
Great Effort ◽  
Robust Performance ◽  
Wide Range ◽  
Objective Quality ◽  
Novel Method

In recent years, PCs have become very popular for a wide range of applications, such as immersive virtual reality scenarios. As a consequence, in the last couple of years, there has been a great effort to develop novel acquisition, representation, compression, and transmission solutions for PC contents in the research community. In particular, the development of objective quality assessment methods that are able to predict the perceptual quality of PCs. In this paper, we present an effective novel method for assessing the quality of PCs, which is based on descriptors that extract perceptual color distance-based texture information of PC contents, called Perceptual Color Distance Patterns (PCDP). In this framework, the statistics of the extracted information are used to model the PC visual quality. Experimental results show that the proposed framework exhibit good and robust performance when compared with several state-of-the-art point cloud quality assessment (PCQA) methods.

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