Analysing the Influence of Vertex Clustering on PCA-Based Dynamic Mesh Compression

Rus, Jan; Váša, Libor

doi:10.1007/978-3-642-14061-7_6

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…Rus and Váša [RV12] proposed a method for smoothing discontinuities between clusters ("mesh deblocking") in meshes processed by clustering-based compression algorithms, such as FAMC [MSK * 08] and Clustered Coddyac [RV10]. Such algorithms divide the mesh into several connected subsets (clusters) based on a certain criterion (similar motion of the vertices in a cluster, for example) and then encode these subsets separately.…”

Section: Application To Dynamic Mesh Compressionmentioning

confidence: 99%

Perceptual Metrics for Static and Dynamic Triangle Meshes

Corsini

Larabi

Lavoué

et al. 2013

Computer Graphics Forum

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Almost all mesh processing procedures cause some more or less visible changes in the appearance of objects represented by polygonal meshes. In many cases, such as mesh watermarking, simplification or lossy compression, the objective is to make the change in appearance negligible, or as small as possible, given some other constraints. Measuring the amount of distortion requires taking into account the final purpose of the data. In many applications, the final consumer of the data is a human observer, and therefore the perceptibility of the introduced appearance change by a human observer should be the criterion that is taken into account when designing and configuring the processing algorithms. In this review, we discuss the existing comparison metrics for static and dynamic (animated) triangle meshes. We describe the concepts used in perception-oriented metrics used for 2D image comparison, and we show how these concepts are employed in existing 3D mesh metrics. We describe the character of subjective data used for evaluation of mesh metrics and provide comparison results identifying the advantages and drawbacks of each method. Finally, we also discuss employing the perception-correlated metrics in perception-oriented mesh processing algorithms.

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Section: Application To Dynamic Mesh Compressionmentioning

confidence: 99%

Perceptual Metrics for Static and Dynamic Triangle Meshes

Corsini

Larabi

Lavoué

et al. 2013

Computer Graphics Forum

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“…Another approach by Rus and Váša (Rus and Váša, 2010) is based on the Coddyac (Váša and Skala, 2007) compression algorithm with focus on the behaviour of trajectory-space PCA with respect to vertex movement complexity. This algorithm transforms geometry information of a dynamic mesh into the form of a set of vertex trajectories and processes this set by PCA.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, we propose a method of postprocessing of decompressed triangle meshes, which locally improves the visual quality with focus on compression algorithms including mesh division techniques. We choose clustered Coddyac (Rus and Váša, 2010) as a representative of this type of algorithms in this paper, because it is currently the most efficient compression algorithm for dynamic meshes.…”

Section: Introductionmentioning

confidence: 99%

Deblocking for Dynamic Triangle Meshes

Rus

Váša

2012

Proceedings of the International Conference on Computer Graphics Theory and Applications

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Mesh segmentation (clustering) is a useful tool, which improves compression performance. On the other hand, per-partes processing of meshes often leads to new types of artifacts-cracks and shifts on the borders between clusters. These artifacts are detected by both, Human Visual System (HVS) and perceptually-motivated distortion metrics. In this paper, we present a post processing algorithm, which aims at reducing such artifacts without needing any additional data-using only information about the cluster distribution that is already present at the decoder. A rigid transformation, which minimises the border artifacts, is iteratively computed and applied per cluster. Our experiments show that this approach leads to a reduction of distortion, as measured by the STED metric, by up to 18% for low bitrates. We also present visual results confirming that the improvement is well visible.

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