A wavelet visible difference predictor

Abstract

Abstract-In this paper, we describe a model of the human visual system (HVS) based on the wavelet transform. This model is largely based on a previously proposed model, but has a number of modifications that make it more amenable to potential integration into a wavelet based image compression scheme. These modifications include the use of a separable wavelet transform instead of the cortex transform, the application of a wavelet contrast sensitivity function (CSF), and a simplified definition of subband contrast that allows us to predict noise visibility directly from wavelet coefficients. Initially, we outline the luminance, frequency, and masking sensitivities of the HVS and discuss how these can be incorporated into the wavelet transform. We then outline a number of limitations of the wavelet transform as a model of the HVS, namely the lack of translational invariance and poor orientation sensitivity. In order to investigate the efficacy of this wavelet based model, a wavelet visible difference predictor (WVDP) is described. The WVDP is then used to predict visible differences between an original and compressed (or noisy) image. Results are presented to emphasize the limitations of commonly used measures of image quality and to demonstrate the performance of the WVDP. The paper concludes with suggestions on how the WVDP can be used to determine a visually optimal quantization strategy for wavelet coefficients and produce a quantitative measure of image quality.Index Terms-Human visual system, image compression, image quality, visible difference prediction, wavelet transform.

Wavelet Based Image Compression Techniques

“…It has been shown in (Bradley, 1999;Nadenau et al, 2003) that the performance of image compression techniques can be significantly improved by exploiting the limitations of the HVS for compression purposes. To achieve this aim, the HVS-model can be embedded in the compression algorithm to optimise the perceived visual quality.…”

“…Research has shown that embedding the Human Visual System (HVS) model into compression algorithms yields significant improvement in the visual quality of the reconstructed images (Aili et al, 2006;Antonini et al, 1992;Bradley, 1999;HSontsch & Karam, 2000;Nadenau et al, 2003;Sheikh Akbari & Soraghan, 2003;Thornton et al, 2002;Voukelatos & Soraghan, 1997). It has been shown in (Bradley, 1999;Nadenau et al, 2003) that the performance of image compression techniques can be significantly improved by exploiting the limitations of the HVS for compression purposes.…”

“…Wavelet-based image coding schemes have proven to be ideally suited for embedding complete HVS models, due to the space-frequency localization properties of the wavelet decompositions (Bradley, 1999). The HVS model has been embedded either at the quantization stage (Aili et al, 2006;HSontsch & Karam, 2000;Nadenau et al, 2003), or at the bit allocation stage (Antonini et al, 1992;Sheikh Akbari & Soraghan, 2003;Thornton et al, 2002;Voukelatos & Soraghan, 1997) of the codec, which yields significant improvement in the visual quality of the reconstructed images.…”

“…The performance of image compression techniques can also be significantly improved by embedding the properties of the Human Visual System (HVS) in their compression algorithms (Bradley, 1999;Nadenau et al, 2003). Due to the space-frequency localization properties of wavelet transforms, wavelet based image codecs are most suitable for embedding the HVS model in their coding algorithm (Bradley, 1999).…”

“…Due to the space-frequency localization properties of wavelet transforms, wavelet based image codecs are most suitable for embedding the HVS model in their coding algorithm (Bradley, 1999). The HVS model can be embedded either in the quantization stage (Aili et al, 2006;HSontsch & Karam, 2000;Nadenau et al, 2003), or at the bit allocation stage (Antonini et al, 1992;Sheikh Akbari & Soraghan, 2003;Thornton et al, 2002;Voukelatos & Soraghan, 1997) of the wavelet based encoders.…”