Signal, Image and Video Processing

, Volume 8, Issue 4, pp 677–686 | Cite as

Artifact reduction in JPEG2000 compressed images at low bit-rate using mathematical morphology filtering

  • Layachi Bennacer
  • Badreddine Bouledjfane
  • Amine Nait-Ali
Original Paper
  • 312 Downloads

Abstract

JPEG2000 is known as an efficient standard to encode images. However, at very low bit-rates, artifacts or distortions can be observed in decoded images. In order to improve the visual quality of decoded images and make them perceptually acceptable, we propose in this work a new preprocessing scheme. This scheme consists in preprocessing the image to be encoded using a nonlinear filtering, considered as a prior phase to JPEG 2000 compression. More specifically, the input image is decomposed into low- and high-frequency sub-images using morphological filtering. Afterward, each sub-image is compressed using JPEG2000, by assigning different bit-rates to each sub-image. To evaluate the quality of the reconstructed image, two different metrics have been used, namely (a) peak signal to noise ratio, to evaluate the visual quality of the low-frequency sub-image, and (b) structural similarity index measure, to evaluate the visual quality of the high-frequency sub-image. Based on the reconstructed images, experimental results show that, at low bit-rates, the proposed scheme provides better visual quality compared to a direct use of JPEG2000 (excluding any preprocessing).

Keywords

Morphological filter JPEG2000  Low bit-rate compression Image quality metrics  PSNR SSIM 

List of symbols

JPEG2000

Joint Photographic Experts Group committee in 2000

DWT

Discrete wavelet transform

DCT

Discrete cosine transform

IDWT

Inverse discrete wavelet transform

\(I(x,y)\)

Image with spatial coordinate \(x\) and \(y\)

\(S\)

Structuring element

\(I\oplus S\)

Dilation of \(I\) by \(S\)

\(I\ominus S\)

Erosion of \(I\) by \(S\)

\(I\circ S\)

Morphological opening of \(I\) by \(S\)

\(I{\bullet } S\)

Morphological closing of \(I\) by \(S\)

\(g, f\)

\(g\) is the mask and \(f\) is the marker

\(R_{g}(f)\)

Reconstruction of g from \(f\)

\(\gamma ^{(\mathrm{rec})}(f, g)\)

Opening by reconstruction of \(g\) from \(f\)

\(I_{\mathrm{Low}}\)

Decomposed image \(I\) at low frequency

\(I_{\mathrm{High}}\)

Decomposed image \(I\) at high frequency

Rate

Compression ratio

\(\alpha \)

Compression ratio of the low-frequency sub-image (bit per pixel)

\(\beta \)

Compression ratio of the high-frequency sub-image (bit per pixel)

\(\varPsi (I_{\mathrm{Low}},\alpha )\)

Compression operator of \(I_{\mathrm{Low}}\) by \(\alpha \)

\(\varPsi (I_\mathrm{High},\beta )\)

Compression operator of \(I_\mathrm{High}\) by \(\beta \)

\(\hbox {Comp}_{\mathrm{Low}}\)

Compressed low-frequency sub-image

\(\hbox {Comp}_{\mathrm{High}}\)

Compressed high-frequency sub-image

\(\varPsi ^{-1}(I_{\mathrm{Low}},\alpha )\)

The inverse compression operator of \(I_{\mathrm{Low}}\) by \(\alpha \)

\(\varPsi ^{-1}(I_{\mathrm{High}},\beta )\)

The inverse compression operator of \(I_{\mathrm{High}}\) by \(\beta \)

bpp

Bit per pixel

MSE

Mean square error

PSNR

Peak signal to noise ratio

SSIM

Structural SIMilarity

\(l()\)

Luminance comparison function

\(c()\)

Contrast comparison function

\(s()\)

Structure comparison function

\(\mu _{f}\)

The average of \(f\)

\(\sigma _{f}^2\)

The variance of \(f\)

\(\sigma _{fg} \)

The covariance between \(f\) and \(g\)

Open image in new window

The dynamic range of the pixel values

\(B\)

The bit depth used for noncompressed image coding

\(C_{1},C_{2}\)

Two variables to stabilize the division with weak denominator

\(\hbox {PSNR}_{\mathrm{New}}\)

Proposed image quality metrics

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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Layachi Bennacer
    • 1
  • Badreddine Bouledjfane
    • 1
  • Amine Nait-Ali
    • 2
  1. 1.Laboratoire d’Etude et de Recherche en Instrumentation et Télécommunications Avancées (LERICA)Université Badji MokhtarAnnabaAlgérie
  2. 2.Laboratoire Images, Signaux et Systèmes Intelligents (LISSI, EA 3956)Université Paris-Est Créteil (UPEC)CréteilFrance

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