public class SynWTFilterIntLift5x3 extends SynWTFilterInt
See the SynWTFilter class for details such as normalization, how to split odd-length signals, etc. In particular, this method assumes that the low-pass coefficient is computed first.
SynWTFilter,
SynWTFilterIntWT_FILTER_FLOAT_CONVOL, WT_FILTER_FLOAT_LIFT, WT_FILTER_INT_LIFTCOC, COD, COM, CRG, EOC, EPH, EPH_LENGTH, ERS_SEG_SYMBOLS, ERS_SOP, MAX_COMP_BITDEPTH, MAX_LPPM, MAX_LPPT, PLM, PLT, POC, PPM, PPT, PRECINCT_PARTITION_DEF_SIZE, QCC, QCD, RCOM_GEN_USE, RGN, RSIZ_BASELINE, RSIZ_ER_FLAG, RSIZ_ROI, SCOX_HOR_CB_PART, SCOX_PRECINCT_PARTITION, SCOX_USE_EPH, SCOX_USE_SOP, SCOX_VER_CB_PART, SIZ, SOC, SOD, SOP, SOP_LENGTH, SOT, SQCX_EXP_MASK, SQCX_EXP_SHIFT, SQCX_GB_MSK, SQCX_GB_SHIFT, SQCX_NO_QUANTIZATION, SQCX_SCALAR_DERIVED, SQCX_SCALAR_EXPOUNDED, SRGN_IMPLICIT, SSIZ_DEPTH_BITS, TLM| Constructor and Description |
|---|
SynWTFilterIntLift5x3() |
| Modifier and Type | Method and Description |
|---|---|
int |
getAnHighNegSupport()
Returns the negative support of the high-pass analysis filter.
|
int |
getAnHighPosSupport()
Returns the positive support of the high-pass analysis filter.
|
int |
getAnLowNegSupport()
Returns the negative support of the low-pass analysis filter.
|
int |
getAnLowPosSupport()
Returns the positive support of the low-pass analysis filter.
|
int |
getImplType()
Returns the implementation type of this filter, as defined in this
class, such as WT_FILTER_INT_LIFT, WT_FILTER_FLOAT_LIFT,
WT_FILTER_FLOAT_CONVOL.
|
int |
getSynHighNegSupport()
Returns the negative support of the high-pass synthesis filter.
|
int |
getSynHighPosSupport()
Returns the positive support of the high-pass synthesis filter.
|
int |
getSynLowNegSupport()
Returns the negative support of the low-pass synthesis filter.
|
int |
getSynLowPosSupport()
Returns the positive support of the low-pass synthesis filter.
|
boolean |
isReversible()
Returns the reversibility of the filter.
|
boolean |
isSameAsFullWT(int tailOvrlp,
int headOvrlp,
int inLen)
Returns true if the wavelet filter computes or uses the same "inner"
subband coefficient as the full frame wavelet transform, and false
otherwise.
|
void |
synthetize_hpf(int[] lowSig,
int lowOff,
int lowLen,
int lowStep,
int[] highSig,
int highOff,
int highLen,
int highStep,
int[] outSig,
int outOff,
int outStep)
An implementation of the synthetize_hpf() method that works on int
data, for the inverse 5x3 wavelet transform using thelifting
scheme.
|
void |
synthetize_lpf(int[] lowSig,
int lowOff,
int lowLen,
int lowStep,
int[] highSig,
int highOff,
int highLen,
int highStep,
int[] outSig,
int outOff,
int outStep)
An implementation of the synthetize_lpf() method that works on int
data, for the inverse 5x3 wavelet transform using the lifting
scheme.
|
String |
toString()
Returns a string of information about the synthesis wavelet filter
|
getDataType, synthetize_hpf, synthetize_lpfpublic void synthetize_lpf(int[] lowSig,
int lowOff,
int lowLen,
int lowStep,
int[] highSig,
int highOff,
int highLen,
int highStep,
int[] outSig,
int outOff,
int outStep)
The coefficients of the first lifting step are [-1/4 1 -1/4].
The coefficients of the second lifting step are [1/2 1 1/2].
synthetize_lpf in class SynWTFilterIntlowSig - This is the array that contains the low-pass input
signal.lowOff - This is the index in lowSig of the first sample to
filter.lowLen - This is the number of samples in the low-pass input
signal to filter.lowStep - This is the step, or interleave factor, of the low-pass
input signal samples in the lowSig array.highSig - This is the array that contains the high-pass input
signal.highOff - This is the index in highSig of the first sample to
filter.highLen - This is the number of samples in the high-pass input
signal to filter.highStep - This is the step, or interleave factor, of the
high-pass input signal samples in the highSig array.outSig - This is the array where the output signal is placed. It
should be long enough to contain the output signal.outOff - This is the index in outSig of the element where to put
the first output sample.outStep - This is the step, or interleave factor, of the output
samples in the outSig array.SynWTFilter.synthetize_lpf(java.lang.Object, int, int, int, java.lang.Object, int, int, int, java.lang.Object, int, int)public void synthetize_hpf(int[] lowSig,
int lowOff,
int lowLen,
int lowStep,
int[] highSig,
int highOff,
int highLen,
int highStep,
int[] outSig,
int outOff,
int outStep)
The coefficients of the first lifting step are [-1/4 1 -1/4].
The coefficients of the second lifting step are [1/2 1 1/2].
synthetize_hpf in class SynWTFilterIntlowSig - This is the array that contains the low-pass input
signal.lowOff - This is the index in lowSig of the first sample to
filter.lowLen - This is the number of samples in the low-pass input
signal to filter.lowStep - This is the step, or interleave factor, of the low-pass
input signal samples in the lowSig array.highSig - This is the array that contains the high-pass input
signal.highOff - This is the index in highSig of the first sample to
filter.highLen - This is the number of samples in the high-pass input
signal to filter.highStep - This is the step, or interleave factor, of the
high-pass input signal samples in the highSig array.outSig - This is the array where the output signal is placed. It
should be long enough to contain the output signal.outOff - This is the index in outSig of the element where to put
the first output sample.outStep - This is the step, or interleave factor, of the output
samples in the outSig array.SynWTFilter.synthetize_hpf(java.lang.Object, int, int, int, java.lang.Object, int, int, int, java.lang.Object, int, int)public int getAnLowNegSupport()
public int getAnLowPosSupport()
public int getAnHighNegSupport()
public int getAnHighPosSupport()
public int getSynLowNegSupport()
public int getSynLowPosSupport()
public int getSynHighNegSupport()
public int getSynHighPosSupport()
public int getImplType()
public boolean isReversible()
public boolean isSameAsFullWT(int tailOvrlp,
int headOvrlp,
int inLen)
The result depends on the length of the allowed overlap when compared to the overlap required by the wavelet filter. It also depends on how overlap processing is implemented in the wavelet filter.
tailOvrlp - This is the number of samples in the input signal
before the first sample to filter that can be used for overlap.headOvrlp - This is the number of samples in the input signal
after the last sample to filter that can be used for overlap.inLen - This is the lenght of the input signal to filter.The
required number of samples in the input signal after the last sample
depends on the length of the input signal.Copyright © 2015. All rights reserved.