373 research outputs found
Modeling and Compressing 3-D Facial Expressions Using Geometry Videos
Singapore National Research Foundatio
Low-latency compression of mocap data using learned spatial decorrelation transform
Due to the growing needs of human motion capture (mocap) in movie, video
games, sports, etc., it is highly desired to compress mocap data for efficient
storage and transmission. This paper presents two efficient frameworks for
compressing human mocap data with low latency. The first framework processes
the data in a frame-by-frame manner so that it is ideal for mocap data
streaming and time critical applications. The second one is clip-based and
provides a flexible tradeoff between latency and compression performance. Since
mocap data exhibits some unique spatial characteristics, we propose a very
effective transform, namely learned orthogonal transform (LOT), for reducing
the spatial redundancy. The LOT problem is formulated as minimizing square
error regularized by orthogonality and sparsity and solved via alternating
iteration. We also adopt a predictive coding and temporal DCT for temporal
decorrelation in the frame- and clip-based frameworks, respectively.
Experimental results show that the proposed frameworks can produce higher
compression performance at lower computational cost and latency than the
state-of-the-art methods.Comment: 15 pages, 9 figure
Rate-Distortion Modeling for Bit Rate Constrained Point Cloud Compression
As being one of the main representation formats of 3D real world and
well-suited for virtual reality and augmented reality applications, point
clouds have gained a lot of popularity. In order to reduce the huge amount of
data, a considerable amount of research on point cloud compression has been
done. However, given a target bit rate, how to properly choose the color and
geometry quantization parameters for compressing point clouds is still an open
issue. In this paper, we propose a rate-distortion model based quantization
parameter selection scheme for bit rate constrained point cloud compression.
Firstly, to overcome the measurement uncertainty in evaluating the distortion
of the point clouds, we propose a unified model to combine the geometry
distortion and color distortion. In this model, we take into account the
correlation between geometry and color variables of point clouds and derive a
dimensionless quantity to represent the overall quality degradation. Then, we
derive the relationships of overall distortion and bit rate with the
quantization parameters. Finally, we formulate the bit rate constrained point
cloud compression as a constrained minimization problem using the derived
polynomial models and deduce the solution via an iterative numerical method.
Experimental results show that the proposed algorithm can achieve optimal
decoded point cloud quality at various target bit rates, and substantially
outperform the video-rate-distortion model based point cloud compression
scheme.Comment: Accepted to IEEE Transactions on Circuits and Systems for Video
Technolog
Enhancing Deep Learning Models through Tensorization: A Comprehensive Survey and Framework
The burgeoning growth of public domain data and the increasing complexity of
deep learning model architectures have underscored the need for more efficient
data representation and analysis techniques. This paper is motivated by the
work of (Helal, 2023) and aims to present a comprehensive overview of
tensorization. This transformative approach bridges the gap between the
inherently multidimensional nature of data and the simplified 2-dimensional
matrices commonly used in linear algebra-based machine learning algorithms.
This paper explores the steps involved in tensorization, multidimensional data
sources, various multiway analysis methods employed, and the benefits of these
approaches. A small example of Blind Source Separation (BSS) is presented
comparing 2-dimensional algorithms and a multiway algorithm in Python. Results
indicate that multiway analysis is more expressive. Contrary to the intuition
of the dimensionality curse, utilising multidimensional datasets in their
native form and applying multiway analysis methods grounded in multilinear
algebra reveal a profound capacity to capture intricate interrelationships
among various dimensions while, surprisingly, reducing the number of model
parameters and accelerating processing. A survey of the multi-away analysis
methods and integration with various Deep Neural Networks models is presented
using case studies in different application domains.Comment: 34 pages, 8 figures, 4 table
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