Vertex partitioning based Multiple Description Coding of 3D dynamic meshes

In this paper, we propose a Multiple Description Coding (MDC) method for reliable transmission of compressed time consistent 3D dynamic meshes. It trades off reconstruction quality for error resilience to provide the best expected reconstruction of 3D mesh sequence at the decoder side. The method is based on partitioning the mesh frames into two sets by temporal subsampling and encoding each set independently by a 3D dynamic mesh coder. The encoded independent bitstreams or so-called descriptions are transmitted independently. The 3D dynamic mesh coder is based on predictive coding with spatial and temporal layered decomposition. In addition, the proposed method allows for different redundancy allocations by including a number of encoded spatial layers of the frames in the other set. The algorithm is evaluated with redundancy-rate-distortion curves and it is shown that, when one of the descriptions is lost, acceptable quality can be achieved with around 50% redundancy.

Multiple description coding of animated meshes

Rate-distortion-optimized predictive compression of dynamic 3D mesh sequences

3D triangle meshes are a common form for representing the geometry of static and dynamic 3D objects. They are employed already in many areas, e.g. e-commerce, video games, online museums, CGI or 3D animated films, etc. Static triangle meshes represent only a piecewise linear approximation of complex 3D objects. As a consequence the approximation error can be unacceptably high unless the number of triangles is sufficiently large. On the other hand a large number of triangles makes these meshes cumbersome to handle and expensive to store or to transmit. Consequently, there exists a demand for techniques for efficient compression of static and dynamic 3D meshes. In this article we start with basics on 3D meshes. Thereafter, we explain the key ideas behind different mesh compression approaches for static and dynamic 3D meshes, and highlight their similarities and differences. Finally, we introduce the upcoming MPEG standard for compression of dynamic 3D meshes, which is referred to as FAMC (Frame-based Animated Mesh Compression), and show comparative compression results.

Multiple Description Coding (MDC) is an error resilient coding technique in which multiple streams from a video source are generated, each is individually and mutually decodable. This error resiliency is obtained at the cost of redundancy, and the amount of redundancy depends on the channel loss rate as well as the frame position in the sequence. Due to the nature of video codecs, an erroneous frame will infect the successive frames and causes an unacceptable quality. MDC redundancy allocation more than required leads to compression inefficiency. Therefore, a channel adaptive optimization for frame-wise redundancy allocation is inevitable. In this paper, the MDC scheme known as Mixed Layer Multiple Description Coding (MLMDC) is under consideration for end-to-end distortion modeling and channel adaptive optimization. The model works based on the side and central decoder outputs mismatch. The performance of the model as well as the optimizer are verified by experimental results measured from JM19.0, H.264/AVC reference software. The experiments also show that the optimal MLMDC outperforms the conventional methods for high enough loss rates.

Application of 3D mesh model coding is first presented in this chapter. We then survey the typical existing algorithms in the area of compression of static and dynamic 3D meshes. In an introductory sub-section we introduce basic concepts of 3D mesh models, including data representations, model formats, data acquisitions and 3D display technologies. Furthermore, we introduce several typical 3D mesh formats and give an overview to coding principles of mesh compression algorithms in general, followed by describing the quantitative measures for 3D mesh compression. Then we describe some typical and state-of-the-art algorithms in 3D mesh compression. Compression and streaming of gigantic 3D models are specially introduced. At last, the MPEG4 3D mesh model coding standard is briefed. We conclude this chapter with a discussion providing an overall picture of developments in the mesh coding area and pointing out directions for future research.

It was recently shown that delta-sigma quantization (DSQ) can be used for optimal multiple description (MD) coding of Gaussian sources. The DSQ scheme combined oversampling, prediction, and noise-shaping in order to trade off side distortion for central distortion in MD coding. It was shown that asymptotically in the dimensions of the resampling, prediction, and noise-shaping filters as well as asymptotically in the quantizer dimensions, all rate-distortion points on the symmetric quadratic Gaussian MD rate-distortion function could be achieved. In this work, we show that this somewhat theoretical framework is suitable for practical low-delay MD audio coding. In particular, we design a practical MD audio coder with two descriptions and provide simulations on real audio data. The simulations demonstrate that even when using low-dimensional noise-shaping, prediction, and resampling filters, it is possible to obtain good quality audio in the presence of packet losses. Simulations on real audio reveal that, contrary to existing designs, it is straightforward to obtain a large number of trade-off points between side distortion and central distortion, which makes the proposed coder suitable for a wide range of applications.

We propose a novel multiple description video coding scheme based on pre- and post-processing of video sequences without modifying the actual coding process itself, thus making it compatible with the current standard coding. In pre-processing, adaptive modes of temporal sampling are employed to regulate the motion change between frames before performing odd/even frame splitting, which facilitates good estimation of lost frames in post-processing after side decoding. Significantly, given a central distortion, rate-distortion optimization with Lagrangian formulation is applied in pre-processing to achieve a good tradeoff between bit rate and side distortion. Furthermore, to simplify Lagrangian optimization, a new optimization criterion is proposed based on the correlation of intra- and inter-descriptions. In the experiments, the proposed scheme achieves better performance compared with other tested multiple description coding (MDC) schemes in both an on-off MDC environment and lossy packet networks.

Multiple-description coding (MDC) is a source coding technique which provides an effective way to mitigate the effects of packet errors/loses by making use of multiple channels. The most attractive application of MDC is perhaps the multiple-description video coding (MDVC) in the peer-to-peer (P2P) scenario so as to support simultaneous video streaming to a large population of clients. To this end, a number of MDVC schemes (both non-scalable and scalable) have been proposed in the past few years. However, almost all non-scalable schemes would suffer from the prediction mismatch between the references used at the encoder and decoder sides (for motion compensation); whereas most scalable schemes (involving a base-layer and some enhancement layers) would suffer from the inter-dependency within the enhancement-layer information. In this paper, we keep a common base-layer in all descriptions and propose some novel design of multiple-description scalar quantizer (MDSQ) that is applied on the enhancement-layer. As a result, this scheme can overcome the above drawbacks and more importantly achieve the seamless streaming among all peers.

Multiple-description coding (MDC) provides an effective way to mitigate the effects of packet errors/loses by making use of multiple channels. Perhaps, the most attractive application of MDC is in the peer-to-peer (P2P) scenario to support simultaneous video streaming to a large population of clients. To this end, a number of multiple-description video coding (MDVC) schemes (both non-scalable and scalable) were proposed in the past few years. However, almost all non-scalable schemes would suffer from the prediction mismatch between the references used at the encoder and decoder sides; whereas all scalable schemes (involving a base-layer and some enhancement layers) would suffer from the inter-dependency within the enhancement-layer information. In this paper, we propose a transform-domain MDVC method that can solve these problems and at the same time offer some other interesting features.

In this paper a new method for robust image transmission over lossy channels using distributed multiple description coding is presented. We propose an efficient yet a simple method for encoding each description from multiple description scalar quantizer by exploiting the correlation information among descriptions. In this way the correlated information which we call side information can be extracted at the decoder from each description. The motivation to such a type of encoding is to improve the side decoding and to increase the overall robustness of the scheme. Simulation results show that the side distortion is comparable with the central decoding distortion. Performance evaluation of the proposed approach over packet erasure channel shows 0.2-0.7 dB improvement compared to simple multiple description coding that uses non-distributed multiple description scalar quantizer and demonstrates the effectiveness of the scheme over lossy channels.

Multiple description coding (MDC) is a source coding technique that exploits path diversity to combat packet losses over errorprone channels. In this paper, we proposed a novel drift-free multistate MDC method. At the encoder side, the original video is compressed into multiple independently decodable H.263 streams, each with its own coding structure and prediction process, such that if one stream is lost, the other stream can still be used to produce video with acceptable quality. At the decoder side, each description is considered as a noisy observation of the original video. A Least square-error (LSE) based merge algorithm is proposed to combine the descriptions. The experimental results show that the proposed algorithm has similar coding efficiency to [1], yet with improved error resilience.

In this letter, we present a performance comparison between multiple description coding (MDC) and unequal loss protection (ULP) for progressive image transmission over lossy packet networks. Two optimization criteria are considered, i.e., a multi-quality criterion, when N distinct quality levels are guaranteed at the decoder side, and the optimization of the expected quality at the receiver. We resort to both a semi-analytical approach and simulation results. To enable numerical comparisons, we address a specific MDC algorithm suitable for progressive imaging, and a state-of-the-art ULP algorithm based on Reed Solomon codes. The results, although cannot be generalized to any MDC and ULP methods, are useful to put into evidence some general features that can drive the selection of the most proper technique for the application at hand. In fact, they allow to put into evidence the main advantages and drawbacks of either technique.

Publication in the conference proceedings of EUSIPCO, Poznan, Poland, 2007

Dynamic 3D mesh compression is of great practical important issues in computer graphics and multimedia applications. In this paper, an efficient compression algorithm is proposed to represent animated mesh sequences in a compact way, so that the storage and transmission of dynamic 3D meshes can be accomplished efficiently. The focus of this paper is on the animated mesh sequences with shared connectivity. The proposed method first computes coarse models (low frequency modes) of the animated sequence using the graph Laplacian matrix. Obtained coordinate weights are used at the decoder to reconstruct the coarse models of the sequence. Then, a novel approach is proposed to extract fixed details (high frequency modes or finer features) of the animated mesh. Finally, a details restoration process is applied at the decoder to add details back to the coarse models of the reconstructed sequence. The superiority of the proposed method to the current state of the arts is demonstrated in terms of low data rates for a given degree of perceived distortion.