Rate‐distortion optimized compression of motion capture data

Váša, Libor; Brunnett, Guido

doi:10.1111/cgf.12326

Cited by 12 publications

(27 citation statements)

References 22 publications

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“…This function g(·) will serve to model the difference between the high-rate fixed-length coding result (12) and the corresponding result for our target application, which involves low-rate variable-length coding. Specifically, we take g(·) to be a logarithmic function of the variance ratio:…”

Section: Bit Allocationmentioning

confidence: 99%

“…7(a). For BVH data, the mean error (ME, defined in (25) in Section V-A) has commonly been used as a distortion metric [12], so ME is shown vs. filter length in Fig. 7(b) for BVH sequences.…”

Section: Filter Lengthmentioning

confidence: 99%

“…Together, these strategies reduce the bitrate by 22-26% at the same reconstruction quality compared to the current state-ofthe-art in online MoCap coding [22]. The resulting codec is also competitive with well-known offline BVH codecs [10], [11], [12].…”

Section: Introductionmentioning

confidence: 96%

“…Furthermore, they used a spline-based approximation of the coefficients and applied adaptive quantization with entropy coding for compression. In [12], Váša and Brunnett used PCA and Lagrangian optimization to compress BVH MoCap data. In the BVH MoCap format, where the relative positions of joints are described hierarchically with the help of Euler angles, the parent joint has more influence on the overall distortion than the children joints due to error propagation.…”

Section: Introductionmentioning

confidence: 99%

“…In the BVH MoCap format, where the relative positions of joints are described hierarchically with the help of Euler angles, the parent joint has more influence on the overall distortion than the children joints due to error propagation. Within the Lagrangian framework, the influence of local distortion of each joint was analyzed in [12] and compression was adjusted to minimize the overall distortion.…”

Section: Introductionmentioning

confidence: 99%

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Online MoCap Data Coding With Bit Allocation, Rate Control, and Motion-Adaptive Post-Processing

Kwak

Bajia

2017

IEEE Trans. Multimedia

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Abstract-With the advancements in methods for capturing 3D object motion, motion capture (MoCap) data are starting to be used beyond their traditional realm of animation and gaming in areas such as the arts, rehabilitation, automotive industry, remote interactions, and so on. As the amount of MoCap data increases, compression becomes crucial for further expansion and adoption of these technologies. In this paper, we extend our previous work on low-delay MoCap data compression by introducing two improvements. The first improvement is the bit allocation to long-term and short-term reference MoCap frames, which provides a 10-15% reduction in coded bitrate at the same quality. The second improvement is the post-processing in the form of motion-adaptive temporal low-pass filtering, which is able to provide another 9-13% savings in the bitrate. The experimental results also indicate that the proposed online MoCap codec is competitive with several state-of-the-art offline codecs. Overall, the proposed techniques integrate into a highly effective online MoCap codec that is suitable for low-delay applications, whose implementation is provided alongside this paper to aid further research in the field.

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Section: Bit Allocationmentioning

confidence: 99%

Section: Filter Lengthmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Online MoCap Data Coding With Bit Allocation, Rate Control, and Motion-Adaptive Post-Processing

Kwak

Bajia

2017

IEEE Trans. Multimedia

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Predictive compression of animated 3D models by optimized weighted blending of key‐frames

Hajizadeh

Ebrahimnezhad

2015

Computer Animation & Virtual

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Efficient compression techniques are required for animated mesh sequences with fixed connectivity and time‐varying geometry. In this paper, we propose a key‐frame‐based technique for three‐dimensional dynamic mesh compression. First, key‐frames are extracted from the animated sequence. Extracted key‐frames are then linearly combined using blending weights to predict the vertex locations of the other frames. These blending weights play a key role in the proposed algorithm because the prediction performance and the required number of key‐frames greatly depend on these weights. We present a novel method in order to compute the optimum blending weight that makes it possible to predict location of the vertices of the non‐key frames with the minimum number of key‐frames. The residual prediction errors are finally quantized and encoded using Huffman coding and another heuristic method. Experimental results on different test sequences with various sizes, topologies, and geometries demonstrate the privileged performance of the proposed method compared with the previous techniques. Copyright © 2015 John Wiley & Sons, Ltd.

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Low-latency compression of mocap data using learned spatial decorrelation transform

Hou

Chau

Thalmann

et al. 2016

Computer Aided Geometric Design

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Due to the growing needs of motion capture (mocap) in movie, video games, sports, etc., it is highly desired to compress mocap data for efficient storage and transmission. Unfortunately, the existing compression methods have either high latency or poor compression performance, making them less appealing for timecritical applications and/or network with limited bandwidth. This paper presents two efficient methods to compress mocap data with low latency. The first method processes the data in a frame-by-frame manner so that it is ideal for mocap data streaming. The second one is clip-oriented and provides a flexible tradeoff between latency and compression performance. It can achieve higher compression performance while keeping the latency fairly low and controllable. Observing that mocap data exhibits some unique spatial characteristics, we learn an orthogonal transform to reduce the spatial redundancy. We formulate the learning problem as the least square of reconstruction error regularized by orthogonality and sparsity, and solve it via alternating iteration. We also adopt a predictive coding and temporal DCT for temporal decorrelation in the frame-and clip-oriented methods, respectively. Experimental results show that the proposed methods can produce higher compression performance at lower computational cost and latency than the state-of-the-art methods. Moreover, our methods are general and applicable to various types of mocap data.

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Rate‐distortion optimized compression of motion capture data

Cited by 12 publications

References 22 publications

Online MoCap Data Coding With Bit Allocation, Rate Control, and Motion-Adaptive Post-Processing

Online MoCap Data Coding With Bit Allocation, Rate Control, and Motion-Adaptive Post-Processing

Predictive compression of animated 3D models by optimized weighted blending of key‐frames

Low-latency compression of mocap data using learned spatial decorrelation transform

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