Mr. Evangelos Alexiou Profile

Evangelos Alexiou

at Ecole Polytechnique Fédérale de Lausanne

SPIE Involvement:

Author

Publications (4)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 21 August 2020 Presentation + Paper

Towards neural network approaches for point cloud compression

Evangelos Alexiou, Kuan Tung, Touradj Ebrahimi

Proceedings Volume 11510, 1151008 (2020) https://doi.org/10.1117/12.2569115

KEYWORDS: Clouds, Data modeling, Distortion, Performance modeling, Visualization, Network architectures, Neural networks

Read Abstract +

Point cloud imaging has emerged as an efficient and popular solution to represent immersive visual information. However, the large volume of data generated in the acquisition process reveals the need of efficient compression solutions in order to store and transmit such contents. Several standardization committees are in the process of finalizing efficient compression schemes to cope with the large volume of information that point clouds require. At the same time, recent efforts on learning-based compression approaches have been shown to exhibit good performance in the coding of conventional image and video contents. It is currently an open question how learning-based coding performs when applied to point cloud data. In this study, we extend recent efforts on the matter by exploring neural network implementations for separate, or joint compression of geometric and textural information from point cloud contents. Two alternative architectures are presented and compared; that is, a unified model that learns to encode point clouds in a holistic way, allowing fine-tuning for quality preservation per attribute, and a second paradigm consisting of two cascading networks that are trained separately to encode geometry and color, individually. A baseline configuration from the best-performing option is compared to the MPEG anchor, showing better performance for geometry and competitive performance for color encoding at low bit-rates. Moreover, the impact of a series of parameters is examined on the network performance, such as the selection of input block resolution for training and testing, the color space, and the loss functions. Results provide guidelines for future efforts in learning-based point cloud compression.

Proceedings Article | 17 September 2018 Presentation + Paper

Point cloud subjective evaluation methodology based on reconstructed surfaces

Evangelos Alexious, Antonio M. Pinheiro, Carlos Duarte, Dragan Matković, Emil Dumić, Luis da Silva Cruz, Lovorka Gotal Dmitrović, Marco Bernardo, Manuela Pereira, Touradj Ebrahimi

Proceedings Volume 10752, 107520H (2018) https://doi.org/10.1117/12.2321518

KEYWORDS: Clouds, Visualization, 3D displays, Video, 3D visualizations

Read Abstract +

Point clouds have been gaining importance as a solution to the problem of efficient representation of 3D geometric and visual information. They are commonly represented by large amounts of data, and compression schemes are important for their manipulation transmission and storing. However, the selection of appropriate compression schemes requires effective quality evaluation. In this work a subjective quality evaluation of point clouds using a surface representation is analyzed. Using a set of point cloud data objects encoded with the popular octree pruning method with different qualities, a subjective evaluation was designed. The point cloud geometry was presented to observers in the form of a movie showing the 3D Poisson reconstructed surface without textural information with the point of view changing in time. Subjective evaluations were performed in three different laboratories. Scores obtained from each test were correlated and no statistical differences were observed. Scores were also correlated with previous subjective tests and a good correlation was obtained when compared with mesh rendering in 2D monitors. Moreover, the results were correlated with state of the art point cloud objective metrics revealing poor correlation. Likewise, the correlation with a subjective test using a different representation of the point cloud data also showed poor correlation. These results suggest the need for more reliable objective quality metrics and further studies on adequate point cloud data representations.

Proceedings Article | 17 September 2018 Presentation + Paper

A novel methodology for quality assessment of voxelized point clouds

Eric Torlig, Evangelos Alexiou, Tiago Fonseca, Ricardo de Queiroz, Touradj Ebrahimi

Proceedings Volume 10752, 107520I (2018) https://doi.org/10.1117/12.2322741

KEYWORDS: Quality measurement

Read Abstract +

Recent trends in multimedia technologies indicate a significant growth of interest for new imaging modalities that aim to provide immersive experiences by increasing the engagement of the user with the content. Among other solutions, point clouds denote an alternative 3D content representation that allows visualization of static or dynamic scenes in a more immersive way. As in many imaging applications, the visual quality of a point cloud content is of crucial importance, as it directly affects the user experience. Despite the recent efforts from the scientific community, subjective and objective quality assessment for this type of visual data representation remains an open problem. In this paper, we propose a new, alternative framework for quality assessment of point clouds. In particular, we develop a rendering software, which performs real-time voxelization and projection of the 3D point clouds onto 2D planes, while allowing interaction between the user and the projected views. These projected images are then employed by two-dimensional objective quality metrics, in order to predict the perceptual quality of the displayed stimuli. Benchmarking results, using subjective ratings that were obtained through experiments in two test laboratories, show that our framework provides high predictive power and outperforms the state of the art in objective quality assessment of point cloud imaging.

Proceedings Article | 19 September 2017 Presentation + Paper

On the performance of metrics to predict quality in point cloud representations

Evangelos Alexiou, Touradj Ebrahimi

Proceedings Volume 10396, 103961H (2017) https://doi.org/10.1117/12.2275142

KEYWORDS: Clouds, Multimedia

Read Abstract +

Point clouds are a promising alternative for immersive representation of visual contents. Recently, an increased interest has been observed in the acquisition, processing and rendering of this modality. Although subjective and objective evaluations are critical in order to assess the visual quality of media content, they still remain open problems for point cloud representation. In this paper we focus our efforts on subjective quality assessment of point cloud geometry, subject to typical types of impairments such as noise corruption and compression-like distortions. In particular, we propose a subjective methodology that is closer to real-life scenarios of point cloud visualization. The performance of the state-of-the-art objective metrics is assessed by considering the subjective scores as the ground truth. Moreover, we investigate the impact of adopting different test methodologies by comparing them. Advantages and drawbacks of every approach are reported, based on statistical analysis. The results and conclusions of this work provide useful insights that could be considered in future experimentation.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years