260 likes | 456 Views
ICASSP 2004. http://signal.ece.utexas.edu. Tone Dependent Color Error Diffusion. Vishal Monga and Brian L. Evans. May 20, 2004. Embedded Signal Processing Laboratory The University of Texas at Austin Austin, TX 78712-1084 USA {vishal, bevans}@ece.utexas.edu. Outline. Introduction
E N D
ICASSP 2004 http://signal.ece.utexas.edu Tone Dependent Color Error Diffusion Vishal Monga and Brian L. Evans May 20, 2004 Embedded Signal Processing LaboratoryThe University of Texas at AustinAustin, TX 78712-1084 USA {vishal, bevans}@ece.utexas.edu
Outline • Introduction • High Quality Halftoning Methods • ErrorDiffusion • Direct Binary Search (DBS) • Grayscale Tone Dependent Error Diffusion • Different error filter for each input gray-level • DBS halftone(s) used for filter design • Color Tone Dependent Error Diffusion • Perceptual Model • Error Filter Design • Conclusion & Future Work
Introduction Original Image Threshold at Mid-Gray Dispersed Dot Screening Clustered DotScreening Floyd SteinbergError Diffusion Digital Halftoning: Examples Direct Binary Search
Background difference threshold u(m) x(m) b(m) _ + 7/16 _ + 3/16 5/16 1/16 e(m) shape error compute error Grayscale Error Diffusion Halftoning • 2- D sigma delta modulation [Anastassiou, 1989] • Shape quantization noise into high freq. • Several Enhancements • Variable thresholds, weights and scan paths Error Diffusion current pixel weights Spectrum
Background Direct Binary Search[Analoui, Allebach 1992] - Computationally too expensive for real-time applications e.g. printing - Used in screen design - Practical upper bound for achievable halftone quality
Grayscale TDED Tone dependent threshold modulation b(m) x(m) _ + _ + Tone dependent error filter Midtone regions (21-234) e(m) FFT DBS pattern for graylevel x Halftone pattern for graylevel x FFT Tone Dependent Error Diffusion[Li & Allebach, 2002] • Train error diffusionweights and thresholdmodulation Highlights and shadows (0-20, 235-255) FFT Graylevel patch x Halftone pattern for graylevel x FFT
Color TDED Tone Dependent Color Error Diffusion • Extension of TDED to color • Goal: e.g. for RGB images obtain optimal (in visual quality) error filters with filter weights dependent on input RGB triplet (or 3-tuple) • Applying grayscale TDED independently to the 3 (or 4) color channels ignores the correlation amongst them • Processing: channel-separable or vectorized • Error filters for each color channel (e.g. R, G, B) • Matrix valued error filters [Damera-Venkata, Evans 2001] • Design of error filter key to quality • Take human visual system (HVS) response into account
Color HVS Model C1 C2 C3 Spatial filtering Perceptual color space Perceptual Model [Poirson, Wandell 1997] • Separate image into channels/visual pathways • Pixel based transformation of RGB Linearized CIELab • Spatial filtering based on HVS characteristics & color space
Color TDED Linearized CIELab Color Space • Linearize CIELab space about D65 white point[Flohr, Kolpatzik, R.Balasubramanian, Carrara, Bouman, Allebach, 1993] Yy = 116 Y/Yn – 116 L = 116 f (Y/Yn) – 116 Cx = 200[X/Xn – Y/Yn] a* = 200[ f(X/Xn ) – f(Y/Yn ) ] Cz = 500 [Y/Yn – Z/Zn] b* = 500 [ f(Y/Yn ) – f(Z/Zn ) ] where f(x) = 7.787x + 16/116 0 ≤ x < 0.008856 f(x) = x1/3 0.008856 ≤ x ≤ 1 • Color Transformation • sRGB CIEXYZ YyCx Cz • sRGB CIEXYZ obtained from http://white.stanford.edu/~brian/scielab/
Color TDED HVS Filtering • Filter chrominance channels more aggressively • Luminance frequency response[Näsänen and Sullivan, 1984] L average luminance of display weighted radial spatial frequency • Chrominance frequency response[Kolpatzik and Bouman, 1992] • Chrominance response allows more low frequency chromatic error not to be perceived vs. luminance response
Color TDED Tone Dependent Color Error Diffusion • Design Issues • (256)3 possible input RGB tuples • Criterion for error filter design • Solution • Design error filters along the diagonal line of the color cube i.e. (R,G,B) = {(0,0,0) ; (1,1,1) …(255,255,255)} • 256 error filters for each of the 3 color planes • Color screens are designed in this manner • Train error filters to minimize the visually weighted squared error between the magnitude spectra of a “constant” RGB image and its halftone pattern
Color TDED Input RGB Patch FFT Color Transformation sRGB Yy Cx Cz (Linearized CIELab) FFT Halftone Pattern Perceptual Error Metric
Color TDED Yy HVS Luminance Frequency Response Total Squared Error (TSE) Cx HVS Chrominance Frequency Response HVS Chrominance Frequency Response Cz Perceptual Error Metric • Find error filters that minimize TSE subject to diffusion and non-negativity constraints, m = r, g, b; a (0, 255) (Floyd-Steinberg)
Color TDED Results (a) Original Color Ramp Image (b) Floyd-Steinberg Error Diffusion
Color TDED Results … (c) *Separable application of grayscale TDED (d) Color TDED *Halftone in (c) courtsey Prof. J. P. Allebach and T. Chang at Purdue University
Color TDED Results … • Halftone Detail • Blue section of the color ramp Floyd-Steinberg Grayscale TDED Color TDED
Color TDED Conclusion & Future Work • Color TDED • Worms and other directional artifacts removed • False textures eliminated • Visibility of “halftone-pattern” minimized (HVS model) • More accurate color rendering (than separable application) • Future Work • Incorporate Color DBS in error filter design to enhance homogenity of halftone textures • Design visually optimum matrix valued filters
Original House Image
Color TDED HVS Filtering contd… • Role of frequency weighting • weighting by a function of angular spatial • frequency [Sullivan, Ray, Miller 1991] where p = (u2+v2)1/2 and w – symmetry parameter reduces contrast sensitivity at odd multiples of 45 degrees equivalent to dumping the luminance error across the diagonals where the eye is least sensitive.