COBRA: Compression of the Basis for PCA Represented Animations

Abstract

In this paper, we present an extension of dynamic mesh compression techniques based on PCA. Such representation allows very compact representation of moving 3D surfaces; however, it requires some side information to be transmitted along with the main data. The biggest part of this information is the PCA basis, and since the data can be encoded very efficiently, the size of the basis cannot be neglected when considering the overall performance of a compression algorithm.We present a new work in this area, as none of the papers about PCA based compression really addresses this issue. We will show that for an efficient and accurate encoding there are better choices than even sophisticated algorithms such as LPC.We will present results showing that our approach can reduce the size of the basis by 90% with respect to direct encoding, which can lead to approximately 25% increase of performance of the compression algorithm without any significant loss of accuracy. Such improvement moves the performance of the PCA encoder beyond the performance of current state of the art dynamic mesh compression algorithms, such as the recently adopted MPEG standard, FAMC.

Toward Real-Time and Efficient Compression of Human Time-Varying Meshes

“…The prediction residuals and the PCA basis functions are then encoded. The same authors proposed COBRA [31] as a method for efficiently compressing the PCA basis. Payan and Antonini [32] addressed the problem using wavelets.…”

“…Their work with some improvements, was standardized under MPEG-4 frame-based animated mesh compression [34]. The proposals included in [34], as well as the combination of [30] and [31], are considered to be today's state of the art.…”

Spectral Animation Compression

“…Among them, principle component analysis (PCA) is often used for reducing the dimensions of the vertex displacement information [6][7] . The PCA bases need to be compressed because they are needed to restore the animation [8] . Moreover, PCA can also be combined with clustering and segmentation [9] , Laplace deformation [10] , or mesh simplification [11] for better performance in compressing dynamic animations.…”

Compressing dynamic meshes with geometric laplacians

“…Each vertex trajectory is expressed as a combination of the basis trajectories and the algorithm encodes only the basis trajectory vectors and the coefficients. The same approach has been revised and improved in [VS09], proposing an alternative approach to compress the basis vectors named CO-BRA, and in [VP11], where the CODDYAC algorithm is combined with the Laplacian encoding technique by Sorkine et al…”

“…We use the COBRA algorithm [VS09] to encode B, while the rest of the paper deals with the encoding of the vectors s i . During decoding, each trajectory t i is reconstructed with some loss of precision ast i =B Ts i +t.…”