Image Filtering in the Compressed Domain

  • Jayanta Mukherjee
  • Sanjit K. Mitra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4338)


Linear filtering of images is usually performed in the spatial domain using the linear convolution operation. In the case of images stored in the block DCT space, the linear filtering is usually performed on the sub-image obtained by applying an inverse DCT to the block DCT data. However, this results in severe blocking artifacts caused by the boundary conditions of individual blocks as pixel values outside the boundaries of the blocks are assumed to be zeros. To get around this problem, we propose to use the symmetric convolution operation in such a way that the operation becomes equivalent to the linear convolution operation in the spatial domain. This is achieved by operating on larger block sizes in the transform domain. We demonstrate its applications in image sharpening and removal of blocking artifacts directly in the compressed domain.


Impulse Response Spatial Domain Individual Block Convolution Operation Symmetric Extension 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Smith, B.C., Rowe, L.: Algorithms for manipulating compressed images. IEEE Comput. Graph. Applicat. Mag. 13, 34–42 (1993)CrossRefGoogle Scholar
  2. 2.
    Neri, A., Russo, G., Talone, P.: Inter-block filtering and downsampling in DCT domain. Signal Processing: Image Commun. 6, 303–317 (1994)CrossRefGoogle Scholar
  3. 3.
    Park, H.W., Park, Y.S., Oh, S.K.: L/M-image folding in block DCT domain using symmetric convolution. IEEE Trans. on Image Processing 12, 1016–1034 (2003)CrossRefGoogle Scholar
  4. 4.
    Tang, J., Peli, E.: Image enhancement using a contrast measure in the compressed domain. IEEE Signal Processing Letters 10, 289–292 (2003)CrossRefGoogle Scholar
  5. 5.
    Shen, B., Sethi, I.K., Bhaskaran, V.: DCT convolution and its application in compressed domain. IEEE Trans. on Circuits and Systems for Video Technology 8, 947–952 (1998)CrossRefGoogle Scholar
  6. 6.
    Mukherjee, J., Mitra, S.K.: Arbitrary resizing of images in the DCT space. IEEE Proc. Vision, Image and Signal Processing 152(2), 152–164Google Scholar
  7. 7.
    Park, Y.S., Park, H.W.: Design and analysis of an image resizing filter in the block- DCT domain. IEEE Trans. on Circuits and Systems for Video Technology 14, 274–279 (2004)CrossRefGoogle Scholar
  8. 8.
    Shin, G.S., Kang, M.G.: Transform domain enhanced resizing for a discrete-cosine-transform-based codec. Optical Engineerring 42, 3204–3214 (2003)CrossRefGoogle Scholar
  9. 9.
    Martucci, S.A.: Symmetric convolution and the discrete sine and cosine transforms. IEEE Trans. on Signal Processing 42, 1038–1051 (1994)CrossRefGoogle Scholar
  10. 10.
    Jiang, J., Feng, G.: The spatial relationships of DCT coefficients between a block and its sub-blocks. IEEE Trans. on Signal Processing 50, 1160–1169 (2002)CrossRefGoogle Scholar
  11. 11.
    Loeffer, C., Ligtenberg, A., Moschytz, G.S.: Practical fast 1-D DCT algorithms with 11 multiplications. In: Proc. IEEE Int. Conf. Accoustics, Speech and Signal Processing, May 1989, vol. 2, pp. 988–991 (1989)Google Scholar
  12. 12.
    Dugad, R., Ahuja, N.: A fast scheme for image size change in the compressed domain. IEEE Trans. on Circuits and Systems for Video Technology 11, 461–474 (2001)CrossRefGoogle Scholar
  13. 13.
    Shen, B., Sethi, I.K., Bhaskaran, V.: Adaptive motion vector resampling for compressed video downscaling. IEEE Trans. on Circuits and Systems for Video Technology 9, 929–936 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jayanta Mukherjee
    • 1
  • Sanjit K. Mitra
    • 2
  1. 1.Dept. of Computer Science and EngineeringIndian Institute of TechnologyKharagpurIndia
  2. 2.Dept. of Electrical and Computer EngineeringUniversity of CaliforniaSanta BarbaraUSA

Personalised recommendations