CQuantizer

Davide Pizzolato

Rate me:

4.69/5 (28 votes)

3 Sep 2003CPOL2 min read

151.3K

6.1K

Color quantization using the octree algorithm

Introduction

This article shows an implementation of the octree algorithm used to reduce the number of colors, finding the optimal palette of the image. The applications of the algorithm range from optimized drawing routines, to graphic tools, to file format conversion utilities.

For this purpose, I will use the CQuantizer class, developed in 1996-97 by Jeff Prosise, and described with two articles in the MSDN. The original code has been written to display images on 256-color display devices in the end of the 16-bit era; but nowadays some functions can be replaced, so I changed the memory allocation functions and removed the need of a device context, to make the class a bit more portable.

What's new

In this update, there are only two minor changes. The first is a small bug-fix in CQuantizer::ProcessImage which causes strange results in small images (let's say icons) with 8 bits per pixel or less, when you try to reduce the number of colors down to 16 or less.

The second change is an improvement to avoid a limit of the octree algorithm: the reduction comes on the leafs of the tree, each of them hold 8 colors, and for less than 16 colors, there are only 2 leafs; so the reduction for a really small number of colors needs a different process. This has been implemented in CQuantizer::SetColorTable, with the help of some additional information gathered by CQuantizer::GetPaletteColors.


original true-color image	reduction to 16 colors
reduction to 8 colors	reduction to 4 colors
all images are © Jairo Boudewyn [^]

The images above show some good results, achievable when the chromatic contents are not very complex, but don't pretend the same quality for all the kinds of images.

Class Members & Operations

`CQuantizer (UINT nMaxColors, UINT nColorBits)`	Construction and initialization. `nMaxColors` = maximum number of colors permitted in the palette. `nColorBits` = number of significant bits in each 8-bit color component. For example, `nColorBits` = 6 tells the algorithm to ignore the lower two bits of each color component. A setting of 5 or 6 generally produces a palette that is pleasing to the eye while keeping the octree's memory requirements to a reasonable minimum. `nColorBits` = 8 is required for reduction to 16 colors or less, when the precision is very important.
`ProcessImage (HANDLE hImage)`	Processes the image to find the optimal palette. `hImage` = DIB image handle.
`SetColorTable (RGBQUAD* prgb)`	Transfers the optimized palette to a `RGBQUAD` structure. The size of the structure must be at least `nMaxColors`.
`GetColorCount ()`	Returns the number of colors found in the optimized palette.

Example

This example shows an application of CQuantizer with the CxImage class:

C++

CxImage image("test.bmp",CXIMAGE_FORMAT_BMP);

CQuantizer q(16,8);
q.ProcessImage(image.GetDIB());
RGBQUAD* ppal=(RGBQUAD*)malloc(16*sizeof(RGBQUAD));
q.SetColorTable(ppal);
image.DecreaseBpp(4,ppal);
free(ppal);