Packet loss resilient transmission of 3D models

Date

2007-09-19

Author

Bici, M. Oguz
Norkin, Andrey
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

309
views

0
downloads

This paper presents an efficient joint source-channel coding scheme based on forward error correction (FEC) for three dimensional (3D) models. The system employs a wavelet based zero-tree 3D mesh coder based on Progressive Geometry Compression (PGC). Reed-Solomon (RS) codes are applied to the embedded output bitstream to add resiliency to packet losses. Two-state Markovian channel model is employed to model packet losses. The proposed method applies approximately optimal and unequal FEC across packets. Therefore the scheme is scalable to varying network bandwidth and packet loss rates (PLR). In addition, Distortion-Rate (D-R) curve is modeled to decrease the computational complexity. Experimental results show that the proposed method achieves considerably better expected quality compared to previous packet-loss resilient schemes.

Subject Keywords

Visual communications, Error correction, Computer vision, Multidimensional systems, Wavelet transform, Networks

URI

https://hdl.handle.net/11511/33406

DOI

https://doi.org/10.1109/icip.2007.4379780

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Joint source-channel coding for error resilient transmission of static 3D models Bici, Mehmet Oguz; Norkin, Andrey; Akar, Gözde (2012-01-01) In this paper, performance analysis of joint source-channel coding techniques for error-resilient transmission of three dimensional (3D) models are presented. In particular, packet based transmission scenarios are analyzed. The packet loss resilient methods are classified into two groups according to progressive compression schemes employed: Compressed Progressive Meshes (CPM) based methods and wavelet based methods. In the first group, layers of CPM algorithm are protected unequally by Forward Error Correc...
Autoencoder-Based Error Correction Coding for One-Bit Quantization Balevi, Eren; Andrews, Jeffrey G. (2020-06-01) This paper proposes a novel deep learning-based error correction coding scheme for AWGN channels under the constraint of one-bit quantization in receivers. Specifically, it is first shown that the optimum error correction code that minimizes the probability of bit error can be obtained by perfectly training a special autoencoder, in which "perfectly" refers to converging the global minima. However, perfect training is not possible in most cases. To approach the performance of a perfectly trained autoencoder...
High Rate Communication over One-Bit Quantized Channels via Deep Learning and LDPC Codes Balevi, Eren; Andrews, Jeffrey G. (2020-05-01) This paper proposes a method for designing error correction codes by combining a known coding scheme with an autoencoder. Specifically, we integrate an LDPC code with a trained autoencoder to develop an error correction code for intractable nonlinear channels. The LDPC encoder shrinks the input space of the autoencoder, which enables the autoencoder to learn more easily. The proposed error correction code shows promising results for one-bit quantization, a challenging case of a nonlinear channel. Specifical...
Numerical Computation of Anisotropically Evolving Yield Surfaces Based on Micro to Macro Transitions Birkle, Manuel; Gürses, Ercan; Miehe, Christian (null; 2004-03-26) The paper proposes a numerical computation technique for anisotropically evolving yield surfaces based on micro–to–macro transitions. The underlying model of the finite crystal plasticity and the homogenization approach have previously been formulated by Miehe et al. [2]. The technique proposed has the following two central aspects, firstly a criterion for the determination of the macroscopic yield point is introduced which is based on the physically relevant mechanisms on the microscale and secondly, as a ...
Image generation using only a discriminator network with gradient norm penalty Yeşilçimen, Cansu Cemre; Akbaş, Emre; Department of Computer Engineering (2022-9) This thesis explores the idea of generating images using only a discriminator network by extending a previously proposed method (Tapli, 2021) in several ways. The base method works by iteratively updating the input image, which is pure noise at the beginning while increasing the discriminator's score. We extend the training procedure of the base network by adding the following new losses: (i) total variation, (ii) N-way classification (if labels are available), and (iii) gradient norm penalty on real exam...

Citation Formats

M. O. Bici, A. Norkin, and G. Akar, “Packet loss resilient transmission of 3D models,” 2007, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/33406.