Image analysis – Image enhancement or restoration – Object boundary expansion or contraction
Reexamination Certificate
1999-04-14
2003-06-03
Rogers, Scott (Department: 2624)
Image analysis
Image enhancement or restoration
Object boundary expansion or contraction
C382S268000, C382S308000, C382S205000
Reexamination Certificate
active
06574374
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The invention is related to image processing. More specifically, the invention relates to the enhancement of the image quality by reducing artifacts due to heavy quantization for compression.
2. Description of Related Art
One crucial aspect of imaging is the process of image compression. In digital systems such as digital cameras image compression is utilized to store and transmit a large amount of image information in fewest number of bits possible while still maintaining an image quality that is acceptable. One example of a popular image compression scheme is JPEG (Joint Photographic Experts Group) which is based upon the Discrete Cosine Transform (DCT). Recently, new image compression schemes have been developed utilizing the Discrete Wavelet Transform (DWT). Unlike the DCT which is periodic in nature, the DWT is better suited and often more efficient in representing sharp discontinuities in data such as that present in image edge features. The DWT decomposes an input image into a number of “sub-bands” in multiple levels which are shown in FIG.
1
.
FIG. 1
shows the results of iteratively applying a 2-dimensional (2-D) DWT to an image.
The first iteration of the 2-D DWT decomposes an image I into four sub-bands S
0
, S
1
, S
2
and S
3
. The sub-band S
0
is also referred to as the “LL” sub-band, based on the double low-pass filtering-used to generate it. S
0
(LL) is essentially a scaled approximation of the original image I, and contains the most salient spatial information to the original image. The sub-bands S
1
, S
2
and S
3
contain edge information and when the input image is noisy, also a considerable amount of that noise. The sub-bands S
1
, S
2
, and S
3
are also referred to as HL, LH and HH sub-bands, respectively, due to the various low-pass and high-pass filtering used to generate them. The level
1
sub-bands S
0
, S
1
, S
2
, and S
3
result from applying the 2-D DWT once to the image I. Since LL sub-band is the scaled version of the original image, we can apply the same decomposition in it. If the 2-D DWT is applied again, to the sub-band S
0
, a level
2
2-D DWT is being performed. This would result in the generation of four new sub-bands S
10
, S
11
, S
12
, and S
13
after decomposition to S
0
. After such decomposition, a mechanism known as quantization, which is the mapping of one range of values to a smaller range, is employed to yield compression. One such quantization technique, known as uniform scalar quantization, divides the original data points by a threshold number T (or quantized parameter) greater than or equal to 1, in order to achieve the mapping.
Since the sub-bands S
2
, S
3
and S
1
are perceptually (from a visual standpoint) less significant than the S
0
sub-band, these sub-bands may be more “roughly” quantized (i.e., assigned a higher quantization threshold T) so that the values therein are compressed greater. Such scalar quantization may use one quantization threshold T
i
throughout one sub-band S
i
and then different a T
j
in another sub-band S
j
. Accordingly,
FIG. 1
shows a threshold T
0
applicable to sub-band S
3
, a threshold T
1
applicable to both sub-bands S
1
and S
2
, T
3
applicable to S
13
, T
2
applicable S
11
and S
12
and T
4
applicable to sub-band S
10
. These quantization thresholds maybe selected depending upon the desired level of compression by introducing quantization loss. The higher the quantization threshold, the more compression and hence more possible loss when the compressed image is reconstructed.
Up to a certain level of quantization, the loss incurred may not be visible by the human eyes. Such compression is referred to as visually lossless compression. For the application which warrants more compression beyond a visually lossless scheme, further compression can be achieved by using a higher quantization threshold than that used for visually lossless compression. As a result, however, visible artifacts will occur in the reconstructed image. We can enhance the compression performance, by reducing these visible artifacts.
When DWT coefficients (from applying a DWT upon an image) are then heavily quantized, “Ringing Artifacts” are often produced in the decompressed (reconstructed) image. Due to the well-known Gibbs phenomenon, the Ringing Effect (which leads to Ringing Artifacts) places rings around a homogenous area of an image such as a clear sky or background. To reduce this effect, several digital filtering techniques have been devised, but these, when applied, have the additional effect of introducing smoothness or blurring. By reducing the sharpness of an image, such filtering, used to remove the Ringing Effect, also degrades image quality. It would be desirable to remove ringing artifacts while preserving the sharp edges which are the most perceived features by the human visual system.
SUMMARY
What is disclosed is a method that includes defining a morphological filtering operator designed to remove ringing artifacts in a decompressed image, and then applying that operator upon pixels of the decompressed image that belong to non-texture image regions.
REFERENCES:
patent: 5768440 (1998-06-01), Campanelli et al.
patent: 5819035 (1998-10-01), Devaney et al.
patent: 6297889 (2001-10-01), Loce et al.
patent: 6301386 (2001-10-01), Zhu et al.
S. H. Oguz et al., “Image Coding Ringing Artifact Reduction Using Morphological Post-Filtering”, IEEE Second Workshop on Multimedia Signal Processing, Cat. No. 98EX175, Dec. 1998, pp. 628-633.*
R. A. Peters, II, “A New Algorithm for Image Noise Reduction Using Mathmatical Morphology”, IEEE Transactions On Image Processing, vol. 4, No. 5, May 1995, pp. 554-568.
Blakely , Sokoloff, Taylor & Zafman LLP
Intel Corporation
Rogers Scott
LandOfFree
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