scholarly journals Evaluating the Coding Performance of 360° Image Projection Formats Using Objective Quality Metrics

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 80
Author(s):  
Ikram Hussain ◽  
Oh-Jin Kwon ◽  
Seungcheol Choi
Keyword(s):  
Image Coding ◽  
Real Life ◽  
Ground Truth ◽  
Quality Metrics ◽  
Evaluation Framework ◽  
Jpeg 2000 ◽  
Coding Gain ◽  
Objective Quality ◽  
Ground Truth Image ◽  
Coding Standards

Recently, 360° content has emerged as a new method for offering real-life interaction. Ultra-high resolution 360° content is mapped to the two-dimensional plane to adjust to the input of existing generic coding standards for transmission. Many formats have been proposed, and tremendous work is being done to investigate 360° videos in the Joint Video Exploration Team using projection-based coding. However, the standardization activities for quality assessment of 360° images are limited. In this study, we evaluate the coding performance of various projection formats, including recently-proposed formats adapting to the input of JPEG and JPEG 2000 content. We present an overview of the nine state-of-the-art formats considered in the evaluation. We also propose an evaluation framework for reducing the bias toward the native equi-rectangular (ERP) format. We consider the downsampled ERP image as the ground truth image. Firstly, format conversions are applied to the ERP image. Secondly, each converted image is subjected to the JPEG and JPEG 2000 image coding standards, then decoded and converted back to the downsampled ERP to find the coding gain of each format. The quality metrics designed for 360° content and conventional 2D metrics have been used for both end-to-end distortion measurement and codec level, in two subsampling modes, i.e., YUV (4:2:0 and 4:4:4). Our evaluation results prove that the hybrid equi-angular format and equatorial cylindrical format achieve better coding performance among the compared formats. Our work presents evidence to find the coding gain of these formats over ERP, which is useful for identifying the best image format for a future standard.

A Benchmark of Objective Quality Metrics for HLG-Based HDR/WCG Image Coding

2020 ◽  
Author(s):  
Yasuko Sugito ◽  
Trevor Canham ◽  
Javier Vazquez-Corral ◽  
Marcelo Bertalmio
Keyword(s):  
Image Coding ◽  
Quality Metrics ◽  
Objective Quality

On the performance of objective quality metrics for lightfields

2021 ◽  
Vol 93 ◽  
pp. 116179
Author(s):  
Saeed Mahmoudpour ◽  
Peter Schelkens
Keyword(s):  
Quality Metrics ◽  
Objective Quality

Interplay between JPEG-2000 image coding and quality estimation

2013 ◽  
Author(s):  
Guilherme O. Pinto ◽  
Sheila S. Hemami
Keyword(s):  
Image Coding ◽  
Jpeg 2000 ◽  
Quality Estimation

scholarly journals Towards Expert-Inspired Automatic Criterion to Cut a Dendrogram for Real-Industrial Applications

2021 ◽  
Author(s):  
Shikha Suman ◽  
Ashutosh Karna ◽  
Karina Gibert
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithms ◽  
Computational Cost ◽  
Real Life ◽  
Ground Truth ◽  
Industrial Applications ◽  
Underlying Structure ◽  
Cluster Validity ◽  
Cluster Validity Index ◽  
Number Of Clusters

Hierarchical clustering is one of the most preferred choices to understand the underlying structure of a dataset and defining typologies, with multiple applications in real life. Among the existing clustering algorithms, the hierarchical family is one of the most popular, as it permits to understand the inner structure of the dataset and find the number of clusters as an output, unlike popular methods, like k-means. One can adjust the granularity of final clustering to the goals of the analysis themselves. The number of clusters in a hierarchical method relies on the analysis of the resulting dendrogram itself. Experts have criteria to visually inspect the dendrogram and determine the number of clusters. Finding automatic criteria to imitate experts in this task is still an open problem. But, dependence on the expert to cut the tree represents a limitation in real applications like the fields industry 4.0 and additive manufacturing. This paper analyses several cluster validity indexes in the context of determining the suitable number of clusters in hierarchical clustering. A new Cluster Validity Index (CVI) is proposed such that it properly catches the implicit criteria used by experts when analyzing dendrograms. The proposal has been applied on a range of datasets and validated against experts ground-truth overcoming the results obtained by the State of the Art and also significantly reduces the computational cost.

scholarly journals Semantic segmentation of gonio-photographs via adaptive ROI localisation and uncertainty estimation

2021 ◽  
Vol 6 (1) ◽  
pp. e000898
Author(s):  
Andrea Peroni ◽  
Anna Paviotti ◽  
Mauro Campigotto ◽  
Luis Abegão Pinto ◽  
Carlo Alberto Cutolo ◽  
...  
Keyword(s):  
Region Of Interest ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Uncertainty Estimation ◽  
Depth Of Field ◽  
Clinical Settings ◽  
Proposed Model ◽  
Validation Experiment ◽  
Segmentation Accuracy ◽  
Ground Truth Image

ObjectiveTo develop and test a deep learning (DL) model for semantic segmentation of anatomical layers of the anterior chamber angle (ACA) in digital gonio-photographs.Methods and analysisWe used a pilot dataset of 274 ACA sector images, annotated by expert ophthalmologists to delineate five anatomical layers: iris root, ciliary body band, scleral spur, trabecular meshwork and cornea. Narrow depth-of-field and peripheral vignetting prevented clinicians from annotating part of each image with sufficient confidence, introducing a degree of subjectivity and features correlation in the ground truth. To overcome these limitations, we present a DL model, designed and trained to perform two tasks simultaneously: (1) maximise the segmentation accuracy within the annotated region of each frame and (2) identify a region of interest (ROI) based on local image informativeness. Moreover, our calibrated model provides results interpretability returning pixel-wise classification uncertainty through Monte Carlo dropout.ResultsThe model was trained and validated in a 5-fold cross-validation experiment on ~90% of available data, achieving ~91% average segmentation accuracy within the annotated part of each ground truth image of the hold-out test set. An appropriate ROI was successfully identified in all test frames. The uncertainty estimation module located correctly inaccuracies and errors of segmentation outputs.ConclusionThe proposed model improves the only previously published work on gonio-photographs segmentation and may be a valid support for the automatic processing of these images to evaluate local tissue morphology. Uncertainty estimation is expected to facilitate acceptance of this system in clinical settings.

JPEG 2000 still image coding versus other standards

2000 ◽  
Author(s):  
Diego Santa-Cruz ◽  
Touradj Ebrahimi ◽  
Joel Askelof ◽  
Mathias Larsson ◽  
Charilaos A. Christopoulos
Keyword(s):  
Image Coding ◽  
Jpeg 2000 ◽  
Still Image
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