scholarly journals Reversible denoising and lifting based color component transformation for lossless image compression

2019 ◽  
Vol 79 (17-18) ◽  
pp. 11269-11294 ◽  
Author(s):  
Roman Starosolski
Keyword(s):  
Color Space ◽  
Optical Resolution ◽  
Standard Test ◽  
Jpeg 2000 ◽  
Characteristic Model ◽  
Model Driven ◽  
Space Transformation ◽  
Color Space Transformation ◽  
Jpeg Xr ◽  
Image Component

Abstract An undesirable side effect of reversible color space transformation, which consists of lifting steps (LSs), is that while removing correlation it contaminates transformed components with noise from other components. Noise affects particularly adversely the compression ratios of lossless compression algorithms. To remove correlation without increasing noise, a reversible denoising and lifting step (RDLS) was proposed that integrates denoising filters into LS. Applying RDLS to color space transformation results in a new image component transformation that is perfectly reversible despite involving the inherently irreversible denoising; the first application of such a transformation is presented in this paper. For the JPEG-LS, JPEG 2000, and JPEG XR standard algorithms in lossless mode, the application of RDLS to the RDgDb color space transformation with simple denoising filters is especially effective for images in the native optical resolution of acquisition devices. It results in improving compression ratios of all those images in cases when unmodified color space transformation either improves or worsens ratios compared with the untransformed image. The average improvement is 5.0–6.0% for two out of the three sets of such images, whereas average ratios of images from standard test-sets are improved by up to 2.2%. For the efficient image-adaptive determination of filters for RDLS, a couple of fast entropy-based estimators of compression effects that may be used independently of the actual compression algorithm are investigated and an immediate filter selection method based on the detector precision characteristic model driven by image acquisition parameters is introduced.

Color Space Transformation using Neural Networks

2019 ◽  
Vol 2019 (1) ◽  
pp. 153-158
Author(s):  
Lindsay MacDonald
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Color Space ◽  
Reflectance Spectra ◽  
Network Architectures ◽  
Color Spaces ◽  
Natural Materials ◽  
Space Transformation ◽  
Color Space Transformation

We investigated how well a multilayer neural network could implement the mapping between two trichromatic color spaces, specifically from camera R,G,B to tristimulus X,Y,Z. For training the network, a set of 800,000 synthetic reflectance spectra was generated. For testing the network, a set of 8,714 real reflectance spectra was collated from instrumental measurements on textiles, paints and natural materials. Various network architectures were tested, with both linear and sigmoidal activations. Results show that over 85% of all test samples had color errors of less than 1.0 ΔE2000 units, much more accurate than could be achieved by regression.

A Correspondence-Free Color Chart Design for Color Calibration

2020 ◽  
Vol 2020 (1) ◽  
pp. 100-104
Author(s):  
Hakki Can Karaimer ◽  
Rang Nguyen
Keyword(s):  
Color Space ◽  
Fixed Number ◽  
New Approach ◽  
Color Chart ◽  
Color Calibration ◽  
Space Transformation ◽  
Color Space Transformation ◽  
Chart Design ◽  
Calibration Approach

Colorimetric calibration computes the necessary color space transformation to map a camera's device-specific color space to a device-independent perceptual color space. Color calibration is most commonly performed by imaging a color rendition chart with a fixed number of color patches with known colorimetric values (e. g., CIE XYZ values). The color space transformation is estimated based on the correspondences between the camera's image and the chart's colors. We present a new approach to colorimetric calibration that does not require explicit color correspondences. Our approach computes a color space transformation by aligning the color distributions of the captured image to the known distribution of a calibration chart containing thousands of colors. We show that a histogram-based colorimetric calibration approach provides results that are onpar with the traditional patch-based method without the need to establish correspondences.

A Novel Piece-Wise Linear Algorithm for Human Skin Segmentation

2015 ◽  
Vol 743 ◽  
pp. 317-320
Author(s):  
Ravi Subban ◽  
Pasupathi Perumalsamy ◽  
G. Annalakshmi
Keyword(s):  
Skin Color ◽  
Color Space ◽  
Skin Detection ◽  
Color Spaces ◽  
Color Component ◽  
Skin Segmentation ◽  
Space Transformation ◽  
Color Space Transformation ◽  
Comprehensive Knowledge ◽  
Novel Method

This paper presents a novel method for skin segmentation in color images using piece-wise linear bound skin detection. Various color schemes are investigated and evaluated to find the effect of color space transformation over the skin detection performance. The comprehensive knowledge about the various color spaces helps in skin color modeling evaluation. The absence of the luminance component increases performance, which also supports in finding the appropriate color space for skin detection. The single color component produces the better performance than combined color component and reduces computational complexity.

scholarly journals Color Edge Detection Based on the Fusion of Intensity and Chromatic Differences

2020 ◽  
Vol 8 (6) ◽  
pp. 1038-1041
Keyword(s):  
Edge Detection ◽  
Color Space ◽  
Mathematical Methods ◽  
Image Intensity ◽  
Space Transformation ◽  
Color Space Transformation ◽  
Intensity Information

Edge detection is the name for a set of mathematical methods which target at classifying points in an image at which the image intensity varies sharply or, has discontinuities. The paper tries to find the solution for detecting color edges based on color and intensity information of two new images H-image and T-image crafted on color space transformation, that will produce two-resulted edges derivates of H-image and T-image and are at last coalesced to obtain final edge.

scholarly journals Compression of image using hybrid combination of haar wavelet transform and color space model in noisy environment

2017 ◽  
Vol 7 (1.5) ◽  
pp. 283 ◽  
Author(s):  
Sridhar C S ◽  
Mahadevan G ◽  
S.K. Khadar Basha ◽  
G. ChenchuKrishnaiah
Keyword(s):  
Image Compression ◽  
Color Image ◽  
Color Space ◽  
Haar Wavelet ◽  
Noisy Environment ◽  
Haar Wavelet Transform ◽  
Security Issues ◽  
Grey Scale ◽  
Space Transformation ◽  
Color Space Transformation

This article manages the examination of image compression utilizing crossover blend of both wavelet change and color space.. The paper covers foundation which incorporates decay of image, thresholding in wavelet for image compression. The compressed image of Haar wavelet is then gone through color space transformation which is utilized to change the color image into grey scale image. On transformation to grey scale image, the number of bits and transfer speed gets diminished. It is conceivable to process the images of various organizations inside the security issues that have been additionally discussed and actualized. The outcomes are shown as depictions in the results. 

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