Sampled image compression methods and image processing pipeline
View Patent ↗A method for processing image or video data is performed in an image processing pipeline. Color filtered mosaiced raw image or video data is received. A one-level wavelet transform of subbands of the color filtered mosaiced raw image or video data to provide LL, HH, LH and HL subbands. The LH and HL subbands are de-correlated by summing and difference operations to provide decorrelated sum and difference subbands. Additional n-level wavelet transformation on the sum and difference subbands and the LL and HH subbands to provide sparsified subbands for encoding. LL and HH and sum subbands are recombined into standard color images e.g., red, green, and blue color components, which are subsequently processed by color correction, white balance, and gamma correction. The sparsified subbands are encoded.
1. A method for processing image or video data performed in an image processing pipeline, the method comprising:
receiving color filtered mosaiced raw image or video data;
conducting a one-level wavelet transform of the color filtered mosaiced raw image or video data to provide LL, HH, LH and HL subbands;
de-correlating the LH and HL subbands by summing and difference operations to provide decorrelated sum and difference subbands;
conducting additional n-level wavelet transformation on the sum and difference subbands and the LL and HH subbands to provide sparsified subbands for encoding, and
encoding the sparsified subbands.
2. The method of claim 1 , wherein the color filtered mosaiced raw image or video data comprises CFA (color filter array) image data.
3. The method of claim 1 , wherein the de-correlating comprises an orthogonal transformation.
4. The method of claim 1 , further comprising quantizing the sum and difference subbands and the LL and HH subbands prior to conducting the additional n-level wavelet transformation.
5. The method of claim 1 , wherein the de-correlating comprises replacing LH (w LH y ) and HL (w HL y ) subband coefficients by decorrelated sum v s y and difference v d y coefficients, according to the following the following:
v
d
y
(
n
)
=
w
LH
y
(
n
)
-
w
HL
y
(
n
)
=
w
LH
g
(
n
)
-
w
HL
g
(
n
)
+
1
/
4
(
w
LL
*
α
(
n
)
-
w
L
⋆
L
α
(
n
)
)
-
1
/
4
(
w
LL
⋆
β
(
n
)
-
w
L
⋆
L
β
(
n
)
)
v
s
y
(
n
)
=
⌊
1
2
(
w
LH
y
(
n
)
+
w
HL
y
(
n
)
)
⌋
=
⌊
1
2
(
{
w
LH
g
(
n
)
+
w
HL
g
(
n
)
+
1
/
4
(
w
LL
⋆
α
(
n
)
+
w
L
⋆
L
α
(
n
)
)
-
1
/
4
(
w
LL
⋆
β
(
n
)
+
w
L
⋆
L
β
(
n
)
)
}
)
⌋
,
wherein * denotes the conjugate wavelet transform.
6. The method of claim 1 , further comprising an initial black level adjustment of the color filtered mosaiced raw image or video data.
7. The method of claim 1 , further comprising, prior to n-level wavelet transformation:
creating a low resolution canonical color space image from the LL, HH, and sum subbands;
correcting the low-resolution image;
conducting a luma/chroma decomposition of the low-resolution image.
8. The method of claim 7 , wherein the creating approximates a quarter resolution color image directly from coefficients of the LL, HH, and sum subbands.
9. The method of claim 1 , wherein the correcting comprises color correction, white balance correction and gamma correction.
10. The method of claim 1 , further comprising quantizing the sum and difference subbands and the LL and HH subbands prior to conducting the additional n-level wavelet transformation and wherein the N-level transform comprises a Daubechies 9/7 transform.