Home Articles Effects of DWT Resolutions in Reduction of Ringing Artifacts in JPEG-2000

Effects of DWT Resolutions in Reduction of Ringing Artifacts in JPEG-2000


Effects of DWT Resolutions in Reduction of Ringing Artifacts in JPEG-2000

Nirendra K.C. and W.A.C. Fernando

Telecommunication program, Asian Institute of Technology
P.O. Box 4, Klong Luang, Patumthani 12120, Thailand.
[email protected]

The importance of visual communications has increased tremendously in the couple of decades as the saying goes “a picture worth thousand words”. Emerging applications such as video-conferencing, cellular video-telephones and multimedia will have a great impact on nowadays professional life, education and entertainment .The digital representation of visual information in its canonic form generates a huge amount of data. It creates three problems:

  • Storage Space
  • Transmission
  • Manipulation

Digital image compression is the solution to the above problems. Image compression is aimed to minimize the number of bits needed to represent an image without sacrificing quality.

In general, compression can be divided into two broad classes:

  1. Lossless
  2. Lossy

In lossless compression, no information is lost, hence, there is no distortion. However we can get low compression ratios, typically 2 or 3 are achieved in practice.

In lossy compression, data redundancies as well as data which are not noticeable to human visual systems are discarded. This produces some distortions. Hence there is trade-off between image quality and compression.

Joint Photographic Expert Group (JPEG) is the one compression expert group. In JPEG compression, the grayscale image is first partitioned into 8×8 blocks that are independently transformed using block Discrete Cosine Transform (DCT), then quantized and entropy coded. JPEG introduces blocking artifacts at medium and high compressions because of its short and nonoverlapping basis functions [1]. JPEG also suffers from ringing artifacts at high compression. Both artifacts are clearly visible at high compression. JPEG2000 is a new generation technique, which can encode images at very low bit-rate with acceptable quality. Because its coder is based on wavelet transform, there will be some ringing artifacts in the reconstructed image. JPEG-2000 outperforms JPEG in terms of compression as well as quality. However, ringing artifacts of images are still a main bottleneck in the JPEG 2000 coding standard. In this paper we have analyzed the effect of different DWT resolutions for ringing artifacts with the same compression.

Rest of the paper is organized is as follows. In section 2, JPEG-2000 is briefly discussed. In section 3, ringing artifacts and its problem are discussed. In section 4, DWT is presented very briefly. Simulation results are presented in section 5. Finally, conclusion is given in section 6.

JPEG-2000
Recently, wavelet transforms have attracted considerable attention with their application to image coding [2]. It is also the transformation technique in JPEG-2000. JPEG-2000 has a long list of features, a subset of which is [3].

  • State-of-the-art low bit-rate compression performance
  • Progressive transmission by quality, resolution, component, or spatial locality
  • Lossy and lossless compression (with lossless decompression available naturally through all types of progression)
  • Random (spatial) access to the bitstream
  • Region of interest coding by progression
  • Continuous and bi-level compressions


Effects of DWT Resolutions in Reduction of Ringing Artifacts in JPEG-2000

Ringing Artifacts
There are different types of distortion or artifacts depending on the compression methods. JPEG, which is based on DCT, suffers from blocking artifacts. JPEG-2000, which is based on DWT, suffers from ringing around edges at high compression. Since edges define the most recognizable features for an object in an image, the distortion around edges are disturbing and annoying to human [4].

In order to achieve higher compression, in frequency domain, we discard high frequencies as human eyes have low sensitivity to high frequency. In spatial domain, a signal is represented by finite number of basis functions. The use of finite series of basis functions approximations to represent the discontinuous waveforms produce Gibbs’ phenomenon. That is, overshoot in the neighborhood of discontinuity. From an image point of view, such overshoots are manifested as ringing artifacts around the point of discontinuity [5]. It appears around edges because they contain many high frequencies. The ringing artifacts image of Lena has been shown in Fig 1.

DWT




DWT transfers iteratively one signal into two or more filtered and decimated signals corresponding to different frequency subbands. A group of transforms coefficients resulting from the same sequence of low pass and high pass filtering operations, both horizontally and vertically are called subbands. The number of decompositions performed on original image to obtain subbands is called subband decomposition level. The total number of subbands for a given K level decomposition is 3K+1. The Figs. 2 and 3 show the number of subbands and resolution levels for K=1 and K=3 respectively.

To perform the forward DWT, the standard uses a 1-D subband decomposition of a 1-D set of samples into low-pass samples and high-pass samples. Lowpass samples represent a downsampled low-resolution version of the original set. High-pass samples represent a downsampled residual version of the original set, needed for the perfect reconstruction of the original set from the low-pass set. The DWT can be irreversible or reversible. The default irreversible transform is implemented by means of the Daubechies 9-tap/7-tap filter [6]. The default reversible transformation is implemented by means of the 5-tap/3-tap filter. The maximum number of resolution levels for DWT is six.

In this paper, effect of the resolutions of DWT on ringing artifacts is considered.

Simulation and Results
In simulations, we used 512 x 512 Lena colored image, Kodak test images of sizes 512 x 768 and Lena black and white picture of size 512 X 512. JasPer software is used for all simulations and to measure ringing artifacts in simulation, we used PSNR as the objective measurement of ringing artifacts. We will compare PSNRs for different resolution with same compression ratio.

In simulations, we used compression rate of .01. Compression rate is the reciprocal of compression ratio. At this compression rate, ringing artifacts were clearly visible in the image. For same compression rate, different DWT resolution levels were applied from K=1 to K=6 to the same image. We observed PSNR for red, blue and green for colored images and found that different values of PSNR for different resolutions of DWT. Tables 1, 2 and 3 show the PSNRs for different DWT resolutions for three different images at the same compression ratio of 0.01. These Tables show that ringing artifacts is a function of number of resolutions of DWT and can be controlled by selecting K adaptively for different images. In this example if we set K=5, we can get the most efficient image in terms of ringing artifacts.

Table 1 Comparison of PSNR’s for 512 x 768 of Kodak test image 01 for different reolutions

Compression
Ratio
Original
size (bits)
No. of
resolutions (K)
After
compression (bits)
PSNR (dB)

RED
GREEN
BLUE

0.01
9395241
1
93389
18.795997
19.116917
18.155765

2
93389
22.085178
21.995923
20.868229

3
93389
23.780908
23.987871
23.396772

4
93389
24.345233
24.527986
24.650753

5
93389
24.472842
24.665972
24.714927

6
93389
24.375507
24.625548
24.704252

Table 2 Comparison of PSNR’s Lena colored image of size 512 x 512 for different resolutions

Compression Ratio
Original (bits)
No. of
resolutions (K)
After
compression (bits)
PSNR (dB)

RED
GREEN
BLUE

0.01
6291456
1
62669
14.835608
17.382185
19.314852

2
62505
21.426299
22.403615
20.806499

3
62505
28.116940
28.457747
27.138719

4
62669
30.337381
29.919802
28.922634

5
62751
30.498237
30.167494
29.289699

6
62669
30.424699
30.163027
29.162789

Table 3 Comparison of PSNR’s of black and white image of Lena 512 x 512 for different resolutions

Compression
Ratio
Original
(bits)

No. of
resolutions (K)

After
compression (bits)

PSNR (dB)

0.01
2097152

1

20398

16.790078

2

20317

19.744508

3

20644

26.882394

4

19661

28.094900

5

20808

28.403192

6

20890

28.367702

Conclusion

In this paper, we analyzed discrete wavelet transform on basis of its resolutions to reduce ringing artifacts. The Tables 1, 2 and 3 clearly show that for same compression ratio for same image, resolution five gives better PSNR than the rest although it is marginal. As Tables show that nearest competitor for resolution 5 is resolution 6. In Table 1, the overall average gain of resolution 5 is of 0.05 dB than the resolution 6. Similarly, Tables 2 and 3 show that gain of 0.07 and 0.04 dB respectively than resolution 6. This clearly means that ringing artifacts are less in resolution 5. The main result of our simulations is that we can adaptively set the resolution number (K) to get less ringing artifacts than any other resolutions for the same compression and for mode integer as described by JasPer software. Future work will be required to reduce ringing artifacts using other features such as changing the scanning pattern and quantization scale. Authors are currently working on this area.

References:

  1. Seungjoon Yang, Yu-Hen Hu,, Truong Q. Nguyen, and Damon L. Tull, “Maximum-Likelihood Parameter Estimation for Image Ringing-Artifact Removal”, IEEE transaction circuit and Systems for Video Technology, Vol. 11, no. 8, August 2001.
  2. A. Said and W. A. Pearlman, “A New Fast and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees,” IEEE Tranaction. on Circuits and Systems for Video Technology, vol. 6, no. 3, pp. 243-250, June 1996.
  3. Michael W. Marcellin, Michael J. Gormish Ali Bilgin, Martin P.Boliek, “An Overview of JPEG-2000” Proceeding. of IEEE Data Compression Conference, pp. 523-541, 2000.
  4. Guoliang Fan, and Wai-Kuen Cham, “Model-Based Edge Reconstruction for Low Bit-Rate Wavelet-Compressed Images”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 10, No. 1, February 2000
  5. Jo Yew Tham, Surendra Ranganath, Ashraf A. Kassim, and Sze Yan Tan “Noniterative Adaptive Post-processing for Ringing Artifact Suppression in Compressed Images”,https:// wavelet.cwaip.nus.edu.sg/papers/isas_ringing.doc
  6. M. Antonini, M. Barlaud, P. Mathieu and I. Daubechies: “Image Coding Using the Wavelet Transform”, IEEE Transaction on. Image Processing, pp. 205-220, April 1992.
  7. Bryan E. Usevitch “A tutorial on Modern Lossy wavelet Image compression: Foundations of JPEG-2000” IEEE signal processing Magazine, September 2001.
  8. Michael D. Adams, “The JPEG 2000 Still Image Compression Standard”, 2001. https://www.ece.ubc.ca/~mdadams.
  9. Shen-Chuan Tai, Yen-Yu Chen, and Shin-Feng Sheu “Design a Morphological De-ringing Filter of Ultrasound Images”, https://par.cse.nsysu.edu.tw/~algo2002/session_paper/A0228.doc
  10. Mohmad Ghanbouri, “Video Coding: An Introduction to Standard Codecs”, IEE Telecommunication series 42, 1999.