Revision 37ebcc92afa8156e87baf04c049b6147f094096b authored by Software Heritage on 22 March 2017, 00:00:00 UTC, committed by Software Heritage on 24 April 2017, 00:00:00 UTC
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Revision metadata
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"url": "https://doi.org/10.5201/ipol.2017.181"
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"atom:title": "ipol_181",
"codemeta:applicationCategory": "Image Processing",
"codemeta:author": {
"codemeta:affiliation": "CTIM, University of Las Palmas de Gran Canaria, Spain",
"codemeta:name": "Javier S\u00e1nchez"
},
"codemeta:dateCreated": "2017-03-22",
"codemeta:datePublished": "2017-04-24",
"codemeta:description": "Midway Video Equalization",
"codemeta:downloadUrl": "http://www.ipol.im/pub/art/2017/181/midway-video_1.0.tar.gz",
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"codemeta:identifier": "ISSN: 2105-1232 DOI: 10.5201/ipol",
"codemeta:name": "Image Processing On Line (IPOL)",
"codemeta:type": "Journal"
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"codemeta:keywords": [
"histogram matching",
"midway equalization",
"dithering method"
],
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"codemeta:abstract": "This article presents an implementation of the 'Midway Equalization' method for videos. This technique allows us to modify the image histograms so that they present similar luminances. We propose two algorithms: the first one based on histogram inversion and the second one on the sorting of images intensities. The former computes the histograms and then finds the contrast change functions by convolving the inverse histograms with a Gaussian function. The latter starts by sorting the pixels of each image by intensity; the temporal signals, composed of all gray levels of the same rank, are then convolved with a Gaussian function. In this sorting method, the resulting histograms are more similar and homogeneous. Nevertheless, the histogram strategy is faster and provides good results in general. The algorithms include a 'dithering' option for reducing quantization artifacts. The whole implementation depends on a single parameter, the standard deviation, that is used for Gaussian convolutions. The experiments show several examples, including the quantization artifacts that appear in some situations and the benefits of dithering. We observe that these artifacts are usually more important in the histogram method.",
"codemeta:identifier": "https://doi.org/10.5201/ipol.2017.181",
"codemeta:name": "Midway Video Equalization",
"codemeta:url": "http://www.ipol.im/pub/art/2017/181/article.pdf"
},
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"codemeta:releaseNotes": "This code implements the algorithm(s) published in the IPOL paper \"Midway Video Equalization\"",
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utils.h
#ifndef UTILS_H
#define UTILS_H
#include <stdlib.h>
size_t read_video(
char *name, //file name
unsigned char *I, //video to write
int size //size of the video
);
size_t write_video(
char *name, //file name
unsigned char *I, //video to write
int size //size of the video
);
void add_noise(
float *u, //input image
float *v, //output noisy image
float sigma, //Gaussian standard deviation
int size //number of pixels
);
void rgb2gray(
unsigned char *rgb, //input color image
float *gray, //output grayscale image
int size //number of pixels
);
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