Skip to content

Hiroki Kojima, Yasuyo Kita, Ichiro Matsuda, Susumu Itoh, Yusuke Kameda, Kyohei Unno, Kei Kawamura: "Improved Probability Modeling for Lossless Image Coding Using Example Search and Adaptive Prediction", Proceedings of the 25th International Workshop on Advanced Image Technology 2022 (IWAIT 2022), Jan. 2022.

Notifications You must be signed in to change notification settings

matsuda-lab-tus/Selective-PMO

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

6 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Selective-PMO

H. Kojima et al., “Improved Probability Modeling for Lossless Image Coding Using Example Search and Adaptive Prediction”, IWAIT2022.

This implementation only supports 8-bpp grayscale images, raw PGM format.

Coding rates (bits per pixel)

Selective-PMO achieves the lowest coding rate (bpp) for most test images compared to state-of-the-art methods.

Image Selective-PMO PMO-GMM MRP Vanilc WLS D TMW Original
Camera 3.804 3.833 3.949 3.995 4.098 8
Couple 3.269 3.281 3.388 3.459 3.446 8
Noisesquare 5.274 5.296 5.270 5.159 5.542 8
Airplane 3.529 3.546 3.591 3.575 3.601 8
Baboon 5.611 5.698 5.663 5.678 5.738 8
Lena 4.215 4.237 4.280 4.246 4.300 8
Lennagrey 3.825 3.845 3.889 3.856 3.908 8
Peppers 4.161 4.176 4.199 4.187 4.251 8
Shapes 0.490 0.497 0.685 1.302 0.740 8
Balloon 2.573 2.584 2.579 2.626 2.649 8
Barb 3.708 3.733 3.815 3.815 4.084 8
Barb2 4.122 4.146 4.216 4.231 4.378 8
Goldhill 4.171 4.191 4.207 4.229 4.266 8
Average 3.750 3.774 3.826 3.874 3.923 8

PMO-GMM

Lossless Image Coding Exploiting Local and Non-local Information via Probability Model Optimization [paper]

MRP

Lossless coding using variable block-size adaptive prediction optimized for each image [paper] [source]

Vanilc WLS D

Probability Distribution Estimation for Autoregressive Pixel-Predictive Image Coding [paper] [source]

TMW

TMW - a New Method for Lossless Image Compression [paper] [source]

Original

All test images are from here.

Installation

  1. Install the following requirements

    • CMake (tested with 3.17.0)
    • GCC (tested with 9.3.0) or Intel C++ Compiler (tested with 2021.2.0)
  2. Generate a build file using CMake.

$ cd bin
$ cmake ..
  1. Compile the project.
$ make

Usage

  1. Print the usage of the options
$ ./pmo --help
  1. Encode (.pgm to .pmo)
$ ./pmo -i /path/to/input/image.pgm -b /path/to/binary/file.pmo
  1. Decode (.pmo to .pgm)
$ ./pmo -b /path/to/binary/file.pmo -o /path/to/output/image.pgm
  1. Encode and Decode ( Check distortion-free )
$ ./pmo -i /path/to/input/image.pgm -b /path/to/binary/file.pmo -o /path/to/output/image.pgm

Authors

Hiroki KOJIMA (Maintainer), Diego FUJII

About

Hiroki Kojima, Yasuyo Kita, Ichiro Matsuda, Susumu Itoh, Yusuke Kameda, Kyohei Unno, Kei Kawamura: "Improved Probability Modeling for Lossless Image Coding Using Example Search and Adaptive Prediction", Proceedings of the 25th International Workshop on Advanced Image Technology 2022 (IWAIT 2022), Jan. 2022.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages