Multiple description coding of 3D dynamic meshes based on temporal subsampling

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 vertices into two sets and encoding each set independently by a 3D dynamic mesh coder. The encoded independent bitstreams or socalled 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 duplicating a number of encoded spatial layers in both sets. The algorithm is evaluated with redundancy-rate-distortion curves and flexible trade-off between redundancy and side distortions can be achieved.

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 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.

Providing reliable video communications over wireless ad-hoc networks is becoming increasingly important as these networks become widely employed in military, homeland defense security, and disaster recovery applications. However, wireless ad-hoc networks have a dynamically changing topology that can cause failures of links and nodes, thus resulting in path loss. Additionally, video communications over wireless ad-hoc networks can suffer from noise and fading effects in the channel. Therefore, it is important to provide error resilience for reliable video communications over such an error-prone network. A number of solutions have been proposed for this problem, including source coding diversity and multipath routing. Source coding diversity methods such as multiple description coding (MDC) have proven to be effective for robust video communications, especially when combined with network path diversity (Gogate et al., 2002; Mao et al., 2003; Apostolopoulos & Trott, 2004). We investigate new MDC methods combined with path diversity to enhance the error resilience of video communications over wireless ad hoc networks. The basic idea of MDC is to encode the video sequence into several descriptions for transmission over multiple paths. Each description can be independently decoded and combined with the other descriptions to provide an acceptable video quality. When more descriptions are received for reconstruction, higher video quality can be achieved. As long as all descriptions are not lost simultaneously, somewhat acceptable quality can be maintained. In order to reduce the likelihood of simultaneous loss of descriptions, different descriptions are transmitted through different paths. This is referred as MDC with path diversity, which reduces the possibility of simultaneous loss of different descriptions and enables load balancing in networks. Many MDC algorithms have been proposed (Goyal, 2001) and they can be divided into three categories: subsampling algorithms in the temporal (Apostolopoulos, 2001), spatial (Franchi et al., 2005) or frequency domain (Reibman et al., 2001), multiple description quantization algorithms (Vaishampayan, 1993; Dumitrescu & Wu, 2009), and multiple description transform coding (Wang et al., 2001). Wang, Reibman & Lin (2005) provides a good review for MDC algorithms. Since subsampling methods are easy to implement and compatible with different video standards, they have been the most commonly investigated MDC algorithms. These methods generally work in the spatial, temporal, or frequency domain to generate multiple 3

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.

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.

Video delivery over error prone channels suffer from packet loss, which significantly deteriorates the reconstructed video quality. Multiple description coding (MDC) is an effective error resilient coding scheme for transmission of video over unreliable networks, where video is encoded into multiple descriptions having redundancy between them. The amount of redundancy between these descriptions has an important role in MDC, because it provides error resilience. In this paper, a new redundancy allocation scheme using visual saliency model is presented with an aim of achieving high decoding quality, when one or more than one descriptions are received at the decoder. The proposed MDC method splits the input video sequence into even and odd subsequences, which are independently encoded by using high efficiency video coding (HEVC) encoder. The missing frames in each description are predicted by using pixel interpolation and motion interpolation. The residual information is generated by using the interpolated frame and its adjacent frame from the other description, which represents the redundancy in each description. Residual information is encoded based on a visual saliency mask, which is generated using a global contrast based visual saliency model. In this work, two different modes are proposed for visual saliency based redundancy allocation to provide better perceptual quality, while decoding the descriptions. The proposed scheme is implemented on HEVC reference software HM 16.2 and compared with two state-of-the-art temporal subsampling based multiple description video coding methods. Experiments show that the proposed method performs better than the reference methods, both in lossless and packet erasure channel conditions.

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.

This paper presents a novel compression scheme for 3D dynamic meshes. The proposed method mainly uses a segmentation approach which exploits both of temporal and spatial dependencies. The temporal coherence of the geometry component is determined using the heat diffusion properties. The motion of the resulting regions is accurately described by 3D affine transforms. These transforms are computed at the first frame to match the subsequent ones. The temporal prediction errors are then encoded. Comparative coding test, for 3D dynamic meshes, were conducted to evaluate the coding efficiency of the proposed compression scheme. Simulations demonstrate that the rate/distortion results are competitive when compared to the state of the art.