A complexity control algorithm for H.264 advanced video coding is proposed. The algorithm can control the
complexity of integer inter motion estimation for a given target complexity. The Rate-Distortion-Complexity
performance is improved by a complexity prediction model, simple analysis of the past statistics and a control
scheme. The algorithm also works well for scene change condition. Test results for coding interlaced video (720
x576 PAL) are reported.
Functional Magnetic Resonance Imaging (fMRI) data sets are four
dimensional (4D) and very large in size. Compression can enhance
system performance in terms of storage and transmission
capacities. Two approaches are investigated: adaptive DPCM and
integer wavelets. In the DPCM approach, each voxel is coded as a
1D signal in time. Due to the spatial coherence of human anatomy
and the similarities in responses of a given substance to stimuli,
we classify the voxels by quantizing autoregressive coefficients
of the associated time sequences. The resulting 2D classification
map is sent as side information. Each voxel time sequence is DPCM
coded using a quantized autoregressive model. The prediction
residuals are coded by simple Rice coding for high decoder
throughput.
In the wavelet approach, the 4D fMRI data set is mapped to a 3D
data set, with the 3D volume at each time instance being laid out
into a 2D plane as a slice mosaic. 3D integer wavelet packets are
used for lossless compression of fMRI data. The wavelet
coefficients are compressed by 3D context-based adaptive
arithmetic coding. An object-oriented compression mode is also
introduced in the wavelet codec. An elliptic mask combined with
the classification of the background is used to segment the
regions of interest from the background.
Significantly higher lossless compression of 4D fMRI than JPEG
2000 and JPEG-LS is achieved by both methods. The 2D
classification map for compression can also be used for image
segmentation in 3D space for analysis and recognition purposes.
This segmentation supports object-based random access to very
large 4D data volumes. The time sequence of DPCM prediction
residuals can be analyzed to yield information on the responses of
the imaged anatomy to the stimuli. The proposed wavelet method
provides an object-oriented progressive (lossy to lossless)
compression of 4D fMRI data set.
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