qccwavwdrencode.3

spiht for linux this is used to decod and encode vedio i wich all enjoy

.TH QCCWDRENCODE 3 "QCCPACK" ""
.SH NAME
QccWAVwdrEncode, QccWAVwdrDecode \-
encode/decode an image using the wavelet-difference-reduction (WDR) algorithm
.SH SYNOPSIS
.B #include "libQccPack.h"
.sp
.BI "int QccWAVwdrEncode(const QccIMGImageComponent *" image ", const QccIMGImageComponent *" mask ", QccBitBuffer *" buffer ", int " num_levels ", const QccWAVWavelet *" wavelet ", const QccWAVPerceptualWeights *" perceptual_weights ", int " target_bit_cnt );
.br
.BI "int QccWAVwdrDecodeHeader(QccBitBuffer *" buffer ", int *" num_levels ", int *" num_rows ", int *" num_cols ", double *" image_mean ", int *" max_coefficient_bits );
.br
.BI "int QccWAVwdrDecode(QccBitBuffer *" buffer ", QccIMGImageComponent *" image ", const QccIMGImageComponent *" mask ", int " num_levels ", const QccWAVWavelet *" wavelet ", const QccWAVPerceptualWeights *" perceptual_weights ", double " image_mean ", int " max_coefficient_bits ", int " target_bit_cnt );
.SH DESCRIPTION
.SS Encoding
.LP
.B QccWAVwdrEncode()
encodes an image component,
.IR image ,
using the WDR algorithm by Tian and Wells. The WDR algorithm involves a 2D-DWT followed by a progressive encoding of the bitplanes of wavelet coefficients. The encoding of each bitplane involves significance-map coding and refinement-bits coding. Significance-map coding encodes the significance of a wavelet coefficient against a given threshold. In WDR, the two-dimensional wavelet pyramid is first mapped into a one-dimensional array according to a scanning order. Each entry in the array is assigned an index. The difference between the indices of two significant coefficients is encoded using index coding. The sign of the significant coefficient is also encoded in significance-map coding. The output symbol stream of significance-map coding is entropy-encoded using arithmetic coding with a 4-symbol context, i.e.
.BR "0", " 1", " +", " -".
.LP
Refinement-bits coding encodes the refining bits of significant coefficients detected before the current bitplane. The output symbol stream is entropy-encoded using arithmetic coding with a 2-symbol context, i.e.
.BR "0 " and " 1".
.LP
.I image
is the image component to be coded,
.I mask
is the mask for the image object (see below),
.I buffer
is the output bitstream following arithmetic coding (must be of
.B QCCBITBUFFER_OUTPUT
type and opened via a prior call to
.BR QccBitBufferStart (3)),
.I num_levels
gives the number of levels of dyadic wavelet decomposition to perform,
.I wavelet
is the wavelet to use for decomposition,
.I perceptual_weights
is the optional perceptual weights to employ (see
.BR QccWAVPerceptualWeights (3);
use
.B NULL
for no perceptual weighting).
.I target_bit_cnt
is the bit budget as an integer.
.LP
.BR QccWAVwdrEncode()
optionally supports the use of a shape-adaptive DWT (SA-DWT) rather than
the usual DWT. That is, 
.BR QccWAVwdrEncode()
can call
.BR QccWAVSubbandPyramidShapeAdaptiveDWT (3)
as the wavelet transform rather than the usual
.BR QccWAVSubbandPyramidDWT (3).
The use of a SA-DWT is indicated by a non-NULL
.IR mask ;
if 
.I mask
is NULL, then the usual DWT is used.
In the case of a SA-DWT,
.I mask 
gives the transparency mask which indicates which pixels of the image
are non-transparent and thus have data that is to be transformed.
Refer to 
.BR QccWAVSubbandPyramidShapeAdaptiveDWT (3)
for more details on the calculation of this SA-DWT.
The wavelet transform is essentially
the only component of the WDR algorithm
that is affected by the shape-adaptive nature of the processing.
That is, transparent regions in the image are effectively
skipped over when the WDR algorithm is initialized;
i.e., transparent coefficients are not added to the lists
of coefficients when the lists are created.
Thus, the transparent coefficients are not
counted in the calculation of runlengths.
Note that the concept of
shape-adaptive coding arose in the recent MPEG-4 standard and was
not considered in the original work by Tian and Wells.
.SS Decoding
.LP
.B QccWAVwdrDecodeHeader()
decodes the header information in the bitstream in the input
.I buffer
(which must be of
.B QCCBITBUFFER_INPUT
type and opened via a prior call to
.BR QccBitBufferStart (3)).
The header information is returned in
.I num_levels
(number of levels of wavelet decomposition),
.I num_rows
(vertical size of image),
.I num_cols
(horizontal size of image),
.I image_mean
(the mean value of the original image), and
.I max_coefficient_bits
(the number of bits used to represent the magnitude of the coefficient with largest absolute value).
.LP
.B QccWAVwdrDecode()
decodes the bitstream
.IR buffer ,
producing the reconstructed image component,
.IR image .
The bitstream must already have had its header read by a prior call
to
.B QccWAVwdrDecodeHeader()
(i.e., you call
.B QccWAVwdrDecodeHeader() 
first and then
.BR QccWAVwdrDecode() ).
If
.I target_bit_cnt
is
.BR QCCENT_ANYNUMBITS ,
then decoding stops when the end of the input bitstream is reached;
otherwise, decoding stops when
.I target_num_bits
from the input bitstream have been decoded.
.LP
If a SA-DWT was used in WDR encoding, then the original transparency
mask should be passed to 
.BR QccWAVwdrDecode()
as
.IR mask .
That is,
.I mask
should be the same transparency mask (in the spatial domain)
that was passed to
.BR QccWAVwdrEncode() .
Note that
.BR QccWAVwdrDecode()
will transform this
.I mask
via a Lazy wavelet transform, and then pass the transformed mask
to 
.BR QccWAVSubbandPyramidInverseShapeAdaptiveDWT (3).
If the usual DWT was used in encoding, then
.I mask
should be a NULL pointer.
.SH "NOTES"
The WDR algorithm as described originally by Tian and
Wells does not employ perceptual weighting or
shape-adaptive coding.
.SH "SEE ALSO"
.BR wdrencode (1),
.BR wdrdecode (1),
.BR imgdwt (1),
.BR imgdist (1),
.BR QccBitBuffer (3),
.BR QccENTArithmeticEncode (3),
.BR QccENTArithmeticDecode (3),
.BR QccWAVPerceptualWeights (3),
.BR QccWAVSubbandPyramid (3),
.BR QccPackWAV (3),
.BR QccPackIMG (3),
.BR QccPack (3)

J. Tian and R. O. Wells, Jr.,
"Embedded Image Coding Using Wavelet Difference Reduction", in
.IR "Wavelet Image and Video Compression" , 
P. N. Topiwala, Ed., pp. 289-302,
Kluwer Academic Publishers, Norwell, MA, 1998.

.SH AUTHOR
Written by Yufei Yuan <[e-mail]>

Copyright (C) 1997-2009  James E. Fowler
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