A Color Contrast Definition for Perceptually Based Color Image Coding

The non-linear nature of the human visual response to achromatic contrast is a key element to improve the performance in achromatic image coding. Expressing transform coefficients in the appropriate contrast units is relevant when some particular non-linear processing hasto be applied. In the achromatic case, the use of non-linear psychophysical models is straightforward since achromatic contrast computation from image transform coefficients is quite simple. However, using equivalent color masking models in transform coding is not easy since psychophysical results are expressed in color contrast units which are non-trivially related to the transform coefficients in opponent color spaces. In this patent we describe a general procedure to define color contrast for any spatial basis functions (such as block-DCT or wavelets) with any chromatic modulation. The proposed definition is based on (1) simple psychophysics to define purely chromatic basis functions, and (2) statistical analysis of the chromatic content of natural images to define the maximum chromatic modulation. The proposed color contrast definition allows for a straightforward extension of the well known non-linear achromatic masking models to the chromatic case for color image coding. In this work, the use of the proposed color contrast definition is illustrated by a particular non-linear color image coding scheme based on blockDCT, non-linear perceptual response transforms in YUV color channels, and non-linear machine learning response selection. This non-linear scheme is compared to the equivalent linear (JPEG-like) scheme, where color contrast definition is not relevant due to its linear nature.

[1]  Gustavo Camps-Valls,et al.  On the Suitable Domain for SVM Training in Image Coding , 2008, J. Mach. Learn. Res..

[2]  Eero P. Simoncelli,et al.  Nonlinear Extraction of Independent Components of Natural Images Using Radial Gaussianization , 2009, Neural Computation.

[3]  Jesús Malo,et al.  Importance of quantiser design compared to optimal multigrid motion estimation in video coding , 2000 .

[4]  Elena Gheorghiu,et al.  Chromatic variations suppress suprathreshold brightness variations. , 2010, Journal of vision.

[5]  C. Stromeyer,et al.  Visual interactions with luminance and chromatic stimuli. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[6]  David H. Brainard,et al.  Detection of chromoluminance patterns on chromoluminance pedestals I: threshold measurements , 2000, Vision Research.

[7]  Eero P. Simoncelli,et al.  Nonlinear image representation for efficient perceptual coding , 2006, IEEE Transactions on Image Processing.

[8]  Jiang Li,et al.  Color Image Coding by using Inter-Color Correlation , 2006, 2006 International Conference on Image Processing.

[9]  Eero P. Simoncelli,et al.  Natural signal statistics and sensory gain control , 2001, Nature Neuroscience.

[10]  D. Heeger Normalization of cell responses in cat striate cortex , 1992, Visual Neuroscience.

[11]  Jorge Herbert de Lira,et al.  Two-Dimensional Signal and Image Processing , 1989 .

[12]  K. Mullen The contrast sensitivity of human colour vision to red‐green and blue‐yellow chromatic gratings. , 1985, The Journal of physiology.

[13]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[14]  Francesc J. Ferri,et al.  Perceptual feedback in multigrid motion estimation using an improved DCT quantization , 2001, IEEE Trans. Image Process..

[15]  Gustavo Camps-Valls,et al.  Perceptual adaptive insensitivity for support vector machine image coding , 2005, IEEE Transactions on Neural Networks.

[16]  Michael W. Marcellin,et al.  JPEG2000 - image compression fundamentals, standards and practice , 2002, The Kluwer International Series in Engineering and Computer Science.

[17]  J. M. Foley,et al.  Detection of chromoluminance patterns on chromoluminance pedestals II: model , 2000, Vision Research.

[18]  Donald I. A. MacLeod,et al.  Color discrimination, color constancy and natural scene statistics , 2002 .

[19]  Francesc J. Ferri,et al.  Regularization operators for natural images based on nonlinear perception models , 2006, IEEE Transactions on Image Processing.

[20]  E. Peli Contrast in complex images. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[21]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[22]  J A Solomon,et al.  Model of visual contrast gain control and pattern masking. , 1997, Journal of the Optical Society of America. A, Optics, image science, and vision.

[23]  Gustavo Camps-Valls,et al.  Perceptual Image Representations for Support Vector Machine Image Coding , 2007 .

[24]  E. Switkes Contrast salience across three-dimensional chromoluminance space , 2008, Vision Research.

[25]  D. H. Kelly Spatiotemporal variation of chromatic and achromatic contrast thresholds. , 1983, Journal of the Optical Society of America.

[26]  P. Lennie,et al.  Chromatic mechanisms in lateral geniculate nucleus of macaque. , 1984, The Journal of physiology.

[27]  Wenjun Zeng,et al.  An overview of the visual optimization tools in JPEG 2000 , 2002, Signal Process. Image Commun..

[28]  Valero Laparra,et al.  Psychophysically Tuned Divisive Normalization Approximately Factorizes the PDF of Natural Images , 2010, Neural Computation.

[29]  Brian A. Wandell,et al.  Color image fidelity metrics evaluated using image distortion maps , 1998, Signal Process..

[30]  Kathy T Mullen,et al.  Ratio model serves suprathreshold color--luminance discrimination. , 2002, Journal of the Optical Society of America. A, Optics, image science, and vision.

[31]  K. Mullen,et al.  The spatial tuning of chromatic mechanisms identified by simultaneous masking , 1994, Vision Research.

[32]  Gregory K. Wallace,et al.  The JPEG still picture compression standard , 1992 .

[33]  J. Malo,et al.  V1 non-linear properties emerge from local-to-global non-linear ICA , 2006, Network.

[34]  Valero Laparra,et al.  Divisive normalization image quality metric revisited. , 2010, Journal of the Optical Society of America. A, Optics, image science, and vision.

[35]  陈普,et al.  Image compression method and image processing apparatus , 2012 .

[36]  Jonathan Robinson,et al.  Combining support vector machine learning with the discrete cosine transform in image compression , 2003, IEEE Trans. Neural Networks.

[37]  Jesús Malo,et al.  Linear transform for simultaneous diagonalization of covariance and perceptual metric matrix in image coding , 2003, Pattern Recognit..

[38]  吴秀美,et al.  The image encoding device , 2011 .

[39]  D. Macleod,et al.  Optimal nonlinear codes for the perception of natural colours , 2001, Network.