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How to Achieve Effective Color Quantization for Animated GIFs in Java?

Linda Hamilton
Release: 2024-11-28 22:16:13
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How to Achieve Effective Color Quantization for Animated GIFs in Java?

Effective Color Quantization for Animated GIFs

In Java, the quantization algorithm you mentioned (found at http://www.java2s.com/Code/Java/2D-Graphics-GUI/Anefficientcolorquantizationalgorithm.htm) appears to be lacking precision for images with more than 256 colors. To enhance color quantization, consider the following alternatives:

Alternative Algorithms:

  • Median cut
  • Population
  • K-means

Approach for Fast and Efficient Quantization:

  • Convert to 15-bit RGB (or 5:6:5 scheme for better results).
  • Create a histogram to count pixel occurrences.
  • Reorder the histogram to eliminate zero values.
  • Sort the histogram to arrange colors in descending order based on pixel count.
  • Create a palette of N colors, adding only unique colors from the sorted histogram until the palette is complete.
  • Map each color in the original image to the closest color in the palette.
  • Recolor the image using the color mapping table.

Sample Code in C :

// Histogram and index arrays
DWORD his[32768];
DWORD idx[32768];

// Recolor mapping table
BYTE recolor[32][32][32];

// Extract 15-bit RGB from 32-bit RGB
cc=((cc>>3)&0x1F)|((cc>>6)&0x3E0)|((cc>>9)&0x7C00);

// Histogram counting
his[cc]++;

// Reorder and sort histogram
for (x=0,y=0;y<32768;y++)
{
    his[x]=his[y];
    idx[x]=idx[y];
    if (his[x]) x++;
}
hists=x;
for (i=1;i;)
    for (i=0,x=0,y=1;y<hists;x++,y++)
        if (his[x]<his[y])
        {
            i=his[x]; his[x]=his[y]; his[y]=i;
            i=idx[x]; idx[x]=idx[y]; idx[y]=i; i=1;
        }

// Create color palette
for (i0=0,x=0;x<hists;x++)
{
    cc=idx[x];
    b= cc     &amp;31;
    g=(cc>> 5)&amp;31;
    r=(cc>>10)&amp;31;
    c0.db[0]=b;
    c0.db[1]=g;
    c0.db[2]=r;
    c0.dd=(c0.dd<<3)&amp;0x00F8F8F8;

    // Find closest color in palette
    int dc=-1; DWORD ii=0;
    for (a=0,i=0;i<i0;i++)
    {
        aa=int(BYTE(c1.db[0]))-int(BYTE(c0.db[0])); if (aa<=0) aa=-aa; a =aa;
        aa=int(BYTE(c1.db[1]))-int(BYTE(c0.db[1])); if (aa<=0) aa=-aa; a+=aa;
        aa=int(BYTE(c1.db[2]))-int(BYTE(c0.db[2])); if (aa<=0) aa=-aa; a+=aa;
        if ((dc<0)||(dc>a)) { dc=a; ii=i; }
    }
    recolor[r][g][b]=ii;
}

// Recolor image using mapping table
pyx [y][x]=lcolor[recolor[r][g][b]];
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This approach provides faster and more accurate color quantization. The specific algorithm and parameters to use may vary depending on the input images and desired outcomes.

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