&lt;title&gt;Quantization of color image components in the DCT domain&lt;/title&gt;

Peterson, Heidi A.; Peng, Huei; Morgan, Jeff; Pennebaker, William B.

doi:10.1117/12.44357

Cited by 57 publications

(29 citation statements)

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“…Some JND models measure the visibility threshold in frequency domain such as sub-band [12]- [14], discrete cosine transform(DCT) [2]- [5] and wavelet domains [15]- [18].…”

Section: B Frequency Domain Jndmentioning

confidence: 99%

“…Based on operating domains, existing JND models can be classified into two categories, namely, spatial domain and frequency domain. Compared with the former category, the latter one is relatively well investigated with DCT decomposition [2]- [5]. Recent progress from local JND to global JND have been made in order to further exploit perceptual redundancy in images.…”

Section: Introductionmentioning

confidence: 99%

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JND modeling: Approaches and applications

Chen

Liu

2014

2014 19th International Conference on Digital Signal Processing

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Just-Noticeable-Difference (JND) measures the smallest detectable difference between two signals therefore can be utilized to quantify the perceivable distortion in the noise contaminated image. In this paper, a comprehensive review of JND models is provided. Specifically, spatial domain and frequency domain JND models are introduced, together with the relevant information about the human visual system. Moreover, the progress from the local JND to global JND is also discussed which aims to exploit the perceptual redundancy in the image. The applications are also discussed in the paper.

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“…Some JND models measure the visibility threshold in frequency domain such as sub-band [12]- [14], discrete cosine transform(DCT) [2]- [5] and wavelet domains [15]- [18].…”

Section: B Frequency Domain Jndmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

JND modeling: Approaches and applications

Chen

Liu

2014

2014 19th International Conference on Digital Signal Processing

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“…The residual from the block is transformed into the frequency domain, using a DCT. This residual is then quantized according to a quality parameter, which is in turn translated into a quantization step size that increases with the spatial frequency of the DCT coefficient, similar to what is proposed by [8]. The nonzero coefficients are encoded in the standard zig-zag-scan order, with zero-run-length encoding and omission of high-frequency, isolated non-zero components.…”

Section: Texture Prediction By Dictionarymentioning

confidence: 99%

Example-based image compression

Cui

Mathur

Covell

et al. 2010

2010 IEEE International Conference on Image Processing

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The current standard image-compression approaches rely on fairly simple predictions, using either block-or wavelet-based methods. While many more sophisticated texture-modeling approaches have been proposed, most do not provide a significant improvement in compression rate over the current standards at a workable encoding complexity level. We re-examine this area, using example-based texture prediction. We find that we can provide consistent and significant improvements over JPEG, reducing the bit rate by more than 20% for many PSNR levels. These improvements require consideration of the differences between residual energy and prediction/residual compressibility when selecting a texture prediction, as well as careful control of the computational complexity in encoding.

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“…In 1990, Peterson 13 proposed the IIP (Image-Independent Perceptual) approach as an experimental estimation of the visibility threshold in DCT transform coefficients.…”

Section: Perceptual Maskmentioning

confidence: 99%

MPEG-4 AVC stream watermarking by m -QIM techniques

Hasnaoui

Belhaj

Mitrea

et al. 2011

Multimedia on Mobile Devices 2011; And Multimedia Content Access: Algorithms and Systems V

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International audienceThe present paper is devoted to the MPEG-4 AVC (a.k.a. H.264) video stream protection by means of watermarking techniques. The embedding process is carried out on quantized index domain and relies on the m-QIM (m-arry Quantisation Index Modulation) principles. In order to cope with the MPEG-4 AVC peculiarities, the Watson's perceptual model is reconsidered and discussed. The experimental results correspond to the MEDIEVALS (a French National Project) corpus of 4 video sequences of about 15 minutes each, encoded at 512kbps. The transparency is assessed by both subjective and objective measures. The transcoding (down to 64kbps) and geometric (StirMark) attacks result in BER of 6.75% and 11.25%, respectively. In order to improve robustness, an MPEG-4 AVC syntax-driven counterattack is considered: this way, the two above mentioned attacks lead to BER of 2% and 10%, respectively. Finally, the overall theoretical relevance of these results is discussed by estimating the related channel capacities

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<title>Quantization of color image components in the DCT domain</title>

Cited by 57 publications

References 0 publications

JND modeling: Approaches and applications

JND modeling: Approaches and applications

Example-based image compression

MPEG-4 AVC stream watermarking by m -QIM techniques

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