Scalable Joint Source and Channel Coding of Meshes

Abstract

This paper proposes a new approach for joint source and channel coding (JSCC) of meshes, simultaneously providing scalability and optimized resilience against transmission errors. An unequal error protection approach is followed, to cope with the different error-sensitivity levels characterizing the various resolution and quality layers produced by the input scalable source codec. The number of layers and the protection levels to be employed for each layer are determined by solving a joint source and channel coding problem. In this context, a novel fast algorithm for solving the optimization problem is conceived, enabling a real-time implementation of the JSCC rate-allocation. An instantiation of the proposed JSCC approach is demonstrated for MeshGrid, which is a scalable 3-D object representation method, part of MPEG-4 AFX. In this context, the L-infinite distortion metric is employed, which is to our knowledge a unique feature in mesh coding. Numerical results show the superiority of the L-infinite norm over the classical L-2 norm in a JSCC setting. One concludes that the proposed joint source and channel coding approach offers resilience against transmission errors, provides graceful degradation, enables a fast real-time implementation, and preserves all the scalability features and animation capabilities of the employed scalable mesh codec.Index Terms-Error resilient coding, joint source and channel coding of meshes, L-infinite coding, MeshGrid, three-dimensional (3-D) graphics, unequal error protection.

Statistical L-infinite distortion estimation in scalable coding of meshes

“…Finally, we point out that, due to its proven accuracy and small computational complexity, the L-infinite distortion metric is also suitable for scalable error-resilient coding of meshes, which we have recently reported in [11].…”

Joint source-channel coding and optimization for mobile video streaming in heterogeneous wireless networks

“…Most of the related work in video transmission focus on (1) finding an optimal bit rate for video coding and channel coding, e.g., [19,20]; (2) designing the video coding mechanism to achieve the target source rate under given channel conditions, e.g., [21]; (3) designing the channel coding to achieve the required reliability, e.g., low-density parity check [22], turbo [23], Reed-Solomon (RS) [24], and fountain [25] codes; (4) designing joint optimization framework, including all available error control components together with error concealment and transmission control, to improve global system performance, e.g., [26]. The authors of [14] deal with the optimal allocation of MPEG-2 encoding and media-independent forward error correction rates under the total given bandwidth.…”

“…In order to have a microscopic view of the results, we also depict the mean values and standard deviations (Stddev) of mobile trajectory 4 in Table 4. The per frame video PSNR during the interval of [0,20] s is presented in Figure 9. It can be observed that FRA-JSCC maintains the PSNR values at a relatively higher range.…”

Efficient error control in 3D mesh coding

“…It is shown experimentally that the accuracy of this second approach is improved substantially, while the supplementary computational expenses are minimal. A statistical, datadependant L-infinite estimator for an intra-band wavelet codec is presented in [1], which in case of MESHGRID has the following expression: reconstructed meshes at a given resolution is upper-bounded by the MAXAD [9]. Specifically:…”

“…Note that, in the proof of (8), it is assumed that a bijective mapping exists between the vertices in A and A . For more details, the interested reader is referred to [9]. Expression (8) implies that if such a bijective mapping exists, then an L-infinite-constrained coding actually implies Hausdorff-distance-constrained coding [1].…”