The document discusses a new image compression scheme that incorporates a model of the human visual system (HVS) to improve both quantitative and qualitative performance over existing techniques like JPEG2000. It presents a new HVS model with two components: contrast sensitivity function (CSF) masking, which weights wavelet coefficients by human sensitivity to spatial frequencies, and asymmetric compression, which more severely quantizes chrominance than luminance. Test results showed the new HVS scheme improved peak signal-to-noise ratio and subjective quality for color images over JPEG2000, and improved subjective quality even with lowered PSNR for grayscale images. The paper proposes several avenues for further improvement.

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RGB Image Compression Using Two Dimensional Discrete Cosine Transform
International Journal of Engineering Trends and Technology
Abstract:
Recent research in transform-based image compression has focused on the wavelet transform due
to its superior performance over other transforms. Performance is often measured solely in terms of
peak signal-to-noise ratio (PSNR) and compression algorithms are optimized for this quantitative
metric. The performance in terms of subjective quality is typically not evaluated. Moreover, the
sensitivities of the human visual system (HVS) are often not incorporated into compression schemes.
This thesis develops new wavelet models of the HVS and illustrates their performance for various
scalar wavelets and multi-wavelet transforms. The performance is measured quantitatively (PSNR)
and qualitatively using our new perceptual testing procedure.
Our new HVS model is comprised of two components: CSF masking and asymmetric compression.
CSF masking weights the wavelet coefficients according to the contrast sensitivity function (CSF)–a
model of humans’ sensitivity to spatial frequency. This mask gives the most perceptible information
the highest priority in the quantizer. The second component of our HVS model is called asymmetric
compression. It is well known that humans are more sensitive to luminance stimuli than they are to
chrominance stimuli; asymmetric compression quantizes the chrominance spaces more severely
than the luminance component.
The results of extensive trials indicate that our HVS model improves both quantitative and
qualitative performance. These trials included 14 observers, 4 gray-scale images and 10 color images
(both natural and synthetic). For gray-scale images, although our HVS scheme lowers PSNR, it
improves subjective quality. For color images, our HVS model improves both PSNR and subjective
quality. A benchmark for our HVS method is the latest version of the international image
compression standard–JPEG2000. In terms of subjective quality, our scheme is superior to JPEG2000
for all images; it also outperforms JPEG2000 by 1 to 3 dB in PSNR.

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(a) Block Diagram
(b) Block diagram of the Color Image Compression Algorithm
(c) The Analysis and Synthesis Stages of a 2-D, 1 Level Scalar Wavelet Decomposition

3. Base paper: - http://www.ijettjournal.org/volume-4/issue-4/IJETT-V4I4P263.pdf
(d) Comparison of One-Level (a) Scalar Wavelet, and (b) Multi-wavelet Decomposition
(e) The Analysis Stage of a 2-D, 1-Level Multi-wavelet decomposition with r = 2

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(f) Block Diagram of CSF Masking Method

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A: -

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B: -

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C: -

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D: -

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E: -

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Conclusion and Future Enhancements:
This thesis provides the most comprehensive results to date for wavelet-based image compression
with a consideration for the HVS. Although our results indicate better quantitative and qualitative
performance than previous research, avenues for improvement remain.
1. The CSF masking operation presented in this thesis applies to scalar wavelets and multi-wavelets.
We believe that the same idea can be extended to wavelet packets and multiwavelet packets.
However, constructing a CSF mask for wavelet packets and multiwavelet packets is not trivial. We
anticipate that constructing and applying a CSF mask to a wavelet packet or multiwavelet packet
decomposition will provide quantitative and qualitative improvement similar to what we have seen
for scalar wavelets and multi-wavelets.
2. Our color compression scheme transforms the RGB input image into the YCbCr color space. This is
a standard method–it is used in JPEG. However, other color space models exist and may offer a more
suitable breakdown for wavelet-based compression. Among the other options for color compression
is the opponent color space model that has been used in recent compression research [16].
3. The same HVS compression ideas used here for still images may provide similar improvement for
digital video compression. CSF masking is a simple operation that can be applied on a frame by
frame basis while adding minimal computational overhead.
4. Both quantitatively and qualitatively, the best multiwavelet rarely outperforms the best scalar
wavelet. New multi-filters are continuing to be developed. The family of balanced multi-wavelets
[33] and symmetric FIR balanced multi-wavelets [21] may provide performance superior to the
multi-wavelets used in this thesis.
5. CSF masking was not performed in the two chrominance spaces because humans’ sensitivity to
chrominance stimuli is relatively uniform across frequency. However, potential gain may be found in
changing the wavelet method in each color space. For instance, multi-wavelets may potentially
compress chrominance information better than scalar wavelets. If trends can be found, we can
choose to compress each color space with a particular wavelet method. Such a scheme may provide
additional quantitative and/or qualitative gain.

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