Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
AN ABSTRACTION BASED REDUCED REFERENCE DEPTH PERCEPTION METRIC FOR 3D VIDEO
Date
2012-10-03
Author
NUR YILMAZ, GÖKÇE
Akar, Gözde
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
185
views
0
downloads
Cite This
In order to speed up the wide-spread proliferation of the 3D video technologies (e.g., coding, transmission, display, etc), the effect of these technologies on 3D perception should be efficiently and reliably investigated. Using Full-Reference (FR) objective metrics for this investigation is not practical especially for "on the fly" 3D perception evaluation. Thus, a Reduced Reference (RR) metric is proposed to predict the depth perception of 3D video in this paper. The color-plus-depth 3D video representation is exploited for the proposed metric. Since the significant depth levels of the depth map sequences have great influence on the depth perception of users, they are considered as side information in the proposed RR metric. To determine the significant depth levels, the depth map sequences are abstracted using bilateral filter. Video Quality Metric (VQM) is utilized to predict the depth perception ensured by the significant depth levels due to its well correlation with the Human Visual System (HVS). The performance assessment results present that the proposed RR metric can be utilized in place of a FR metric to reliably measure the depth perception of 3D video with a low overhead.
Subject Keywords
3D Video
,
Bilateral Filter
,
Depth Map Abstraction
,
Depth Perception
,
Reduced Reference Metric
URI
https://hdl.handle.net/11511/55248
Collections
Department of Electrical and Electronics Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
An analysis of stereo depth estimation utilizing attention mechanisms, self-supervised pose estimators & temporal predictions
Oğuzman, Utku; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2022-5-18)
By the recent success of deep learning, real-world applications of stereo depth estimation algorithms attracted the interest of many researchers. Using the available datasets, synthetic or real-world, the researchers begin analyzing their ideas for practical applications. In this thesis, a thorough analysis is performed of such an aim. The state-of-the-art stereo depth estimation algorithms are tried to be improved by incorporating attention mechanisms to the current networks and better initialization strat...
A pixel-by-pixel learned lossless image compression method with parallel decoding
Gümüş, Sinem; Kamışlı, Fatih; Department of Electrical and Electronics Engineering (2022-7)
The success of deep learning in computer vision applications has led to the use of learning based algorithms also in image compression. Learning based lossless image compression algorithms can be divided into three categories, namely, pixel-by-pixel (or masked convolution based) algorithms, prior based algorithms and latent representation based algorithms. In the pixel-by-pixel algorithms, each pixel’s probability distribution is obtained by processing the previously coded left and upper neighbouring pixels...
A modular scheme for 2D/3D conversion of TV broadcast
Knorr, Sebastian; Imre, Evren; Oezkalayci, Burak; Alatan, Abdullah Aydın; Sikora, Thomas (2006-06-16)
The 3D reconstruction from 2D broadcast video is a challenging problem with many potential applications, such as 3DTV, free-viewpoint video or augmented reality. In this paper, a modular system capable of efficiently reconstructing 3D scenes from broadcast video is proposed. The system consists of four constitutive modules: tracking and segmentation, self-calibration, sparse reconstruction and, finally, dense reconstruction. This paper also introduces some novel approaches for moving object segmentation and...
The weight consistency matrix framework for general non-binary LDPC code optimization: Applications in flash memories
Hareedy, Ahmed; Lanka, Chinmayi; Schoeny, Clayton; Dolecek, Lara (2016-08-10)
© 2016 IEEE.Transmission channels underlying modern memory systems, e.g., Flash memories, possess a significant amount of asymmetry. While existing LDPC codes optimized for symmetric, AWGN-like channels are being actively considered for Flash applications, we demonstrate that, due to channel asymmetry, such approaches are fairly inadequate. We propose a new, general, combinatorial framework for the analysis and design of non-binary LDPC (NB-LDPC) codes for asymmetric channels. We introduce a refined definit...
A SPATIOTEMPORAL NO-REFERENCE VIDEO QUALITY ASSESSMENT MODEL
Konuk, Baris; Zerman, Emin; NUR YILMAZ, GÖKÇE; Akar, Gözde (2013-09-18)
Many researchers have been developing objective video quality assessment methods due to increasing demand for perceived video quality measurement results by end users to speed-up advancements of multimedia services. However, most of these methods are either Full-Reference (FR) metrics, which require the original video or Reduced-Reference (RR) metrics, which need some features extracted from the original video. No-Reference (NR) metrics, on the other hand, do not require any information about the original v...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
G. NUR YILMAZ and G. Akar, “AN ABSTRACTION BASED REDUCED REFERENCE DEPTH PERCEPTION METRIC FOR 3D VIDEO,” 2012, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55248.