Finding Optimal Parameter Configuration for a Dynamic Triangle Mesh Compressor

Petřík, Oldřich; Váša, Libor

doi:10.1007/978-3-642-14061-7_4

Cited by 3 publications

(6 citation statements)

References 10 publications

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“…To perform a fair comparison using a particular distortion metric, all the compared compression algorithms need to be optimally configured with respect to that metric. Petřík and Váša proposed an optimisation technique for configuring a dynamic mesh compression algorithm optimally with respect to a chosen metric [PV10]. This technique has been subsequently used, together with the STED metric, in the development of the perception-driven compression algorithm published in [VP11].…”

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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“…The task is to minimize D ( q 1, q 2, …, q n ) under the condition that

for some given target rate r t . The method of Lagrange multiplier yields a condition for the optimum grad ( D ) = λ grad ( R ), which can be rewritten as

which is also known as the principle of equal slopes [PV10]. Note that the intuitive justification of this condition is, that if there was a different slope in a pair of parameters q i and q j , then it would be possible to reach a configuration that has the same rate and smaller distortion by increasing one of these parameters and decreasing the other.…”

Section: Methodsmentioning

confidence: 99%

“…The overall performance depends on the interplay of these parameters. If the optimal compression performance is required, then one can use a procedure similar to the one presented in [PViO] in order to find the optimal configuration. Optimizing the parameters allows reaching virtually any desired accuracy and bitrate, and thus we use it in our experiments to produce results at bitrates comparable to the ones achieved by competing algorithms.…”

Section: Methodsmentioning

confidence: 99%

“…If the optimal compression performance is required, then one can use a procedure similar to the one presented in [PV10] in order to find the optimal configuration. If the optimal compression performance is required, then one can use a procedure similar to the one presented in [PV10] in order to find the optimal configuration.…”

Section: Algorithm Configurationmentioning

confidence: 99%

“…which is also known as the principle of equal slopes [PV10]. Note that the intuitive justification of this condition is, that if there was a different slope in a pair of parameters q i and q j , then it would be possible to reach a configuration that has the same rate and smaller distortion by increasing one of these parameters and decreasing the other.…”

Section: Lagrange Multipliermentioning

confidence: 99%

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Rate‐distortion optimized compression of motion capture data

Váša

Brunnett

2014

Computer Graphics Forum

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Lossy compression of motion capture data can alleviate the problems of efficient storage and transmission by exploiting the redundancy and the superfluous precision of the data. When considering the acceptable amount of distortion, perceptual issues have to be taken into account. Current state of the art methods reduce the data rate required for high quality storage of motion capture data using various techniques. Most of them, however, do not use the common tools of general data compression, such as the method of Lagrange multipliers, and thus they obtain sub-optimal results, making it difficult to do a fair comparison of their performance. In this paper, we present a general preprocessing step based on Lagrange multipliers, which allows to rigorously adjust the precision in each of the degrees of freedom of the input data according to the amount of influence the given degree of freedom has on the overall distortion. We then present a simple compression method based on Principal Component Analysis, which in combination with the proposed preprocessing achieves significantly better results than current state of the art methods. It allows optimization with respect to various distortion metrics, and we discuss the choice of the metric in two common but distinct scenarios, proposing a perceptually oriented comparison metric based on the relation of the problem at hand to the problem of compression of dynamic meshes.

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Sparse Low-Rank Matrix Approximation for Data Compression

Hou

Chau

Thalmann

et al. 2017

IEEE Trans. Circuits Syst. Video Technol.

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Low-rank matrix approximation (LRMA) is a powerful technique for signal processing and pattern analysis. However, its potential for data compression has not yet been fully investigated in the literature. In this paper, we propose sparse lowrank matrix approximation (SLRMA), an effective computational tool for data compression. SLRMA extends the conventional LRMA by exploring both the intra-and inter-coherence of data samples simultaneously. With the aid of prescribed orthogonal transforms (e.g., discrete cosine/wavelet transform and graph transform), SLRMA decomposes a matrix into a product of two smaller matrices, where one matrix is made of extremely sparse and orthogonal column vectors, and the other consists of the transform coefficients. Technically, we formulate SLRMA as a constrained optimization problem, i.e., minimizing the approximation error in the least-squares sense regularized by ℓ0norm and orthogonality, and solve it using the inexact augmented Lagrangian multiplier method. Through extensive tests on realworld data, such as 2D image sets and 3D dynamic meshes, we observe that (i) SLRMA empirically converges well; (ii) SLRMA can produce approximation error comparable to LRMA but in a much sparse form; (iii) SLRMA-based compression schemes significantly outperform the state-of-the-art in terms of ratedistortion performance.

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Finding Optimal Parameter Configuration for a Dynamic Triangle Mesh Compressor

Cited by 3 publications

References 10 publications

Perceptual Metrics for Static and Dynamic Triangle Meshes

Perceptual Metrics for Static and Dynamic Triangle Meshes

Rate‐distortion optimized compression of motion capture data

Sparse Low-Rank Matrix Approximation for Data Compression

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