In this work, we propose two compression algorithms for PointTexture
3D sequences: the octree-based scheme and the motion-compensated
prediction scheme. The first scheme represents each PointTexture
frame hierarchically using an octree. The geometry information in
the octree nodes is encoded by the predictive partial matching (PPM)
method. The encoder supports the progressive transmission of the 3D
frame by transmitting the octree nodes in a top-down manner. The
second scheme adopts the motion-compensated prediction to exploit
the temporal correlation in 3D sequences. It first divides each
frame into blocks, and then estimates the motion of each block using
the block matching algorithm. In contrast to the motion-compensated
2D video coding, the prediction residual may take more bits than the
original signal. Thus, in our approach, the motion compensation is
used only for the blocks that can be replaced by the matching
blocks. The other blocks are PPM-encoded. Extensive simulation
results demonstrate that the proposed algorithms provide excellent
compression performances.
KEYWORDS: 3D modeling, Image compression, Data modeling, 3D image processing, Binary data, Computer programming, Distortion, Cameras, Data conversion, Computer simulations
In this paper, we develop a tree-structured predictive partial matching (PPM) scheme for progressive compression of PointTexture images. By incorporating PPM with tree-structured coding, the proposed
algorithm can compress 3D depth information progressively into a single bitstream. Also, the proposed algorithm compresses color information using a differential pulse coding modulation (DPCM) coder and interweaves the compressed depth and color information effciently. Thus, the decoder can reconstruct 3D models from the coarsest resolution to the highest resolution from a single bitstream. Simulation results demonstrate that the proposed algorithm provides much better compression performance than a universal Lempel-Ziv coder, WinZip.
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