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Introduction display 8-bit graphics in all their display resolutions, but very old computers were limited to even smaller palette sizes
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Bitmap image file formats represent colors in essentially the same way computer displays do Some specify the color value for each pixel, some use a color palette, and others support both methods Table 12 shows the methods used to represent colors for various image formats A file format that uses a palette may use pixel values with fewer than 8 bits in order to reduce the size of the file A 4-bit-per-pixel image requires half as much storage as an 8-bit-per-pixel image of the same size For images that use a limited number of colors, such as cartoons, a simple method to reduce the file
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Table 12 Color Representation Methods
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Palette
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BMP JPEG GIF PNG
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Compression Since color bitmap images typically require over a megabyte of storage, most image file formats incorporate compression techniques Compression techniques take advantage of patterns within the image data to find an equivalent representation that occupies less space Completely random data cannot be compressed The following are brief descriptions of the compression techniques used by the image formats in this book Table 13 shows the techniques used by each format
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Table 13 Compression Methods Used by Various File Formats
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BMP RLE LZ Huffman DCT
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Run Length Encoding (RLE) Consecutive pixels with the same value are encoded using a run length and value pair For example, an image with the pixel value 8 repeated 9 times could be represented as the 2-byte sequence
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0916 0816
rather than
0816 0816 0816 0816 0816 0816 0816 0816
LZ Encoding The compressor maintains a dictionary containing pixel value sequences that have already been encountered The compressed stream contains codes that represent entries in the dictionary Huffman Coding Rather than using a fixed number of bits to represent component values, variable length codes are used More frequently used values are assigned shorter codes Discrete Cosine Transform (DCT) Blocks of pixels are represented using cosine functions of different frequencies The high frequencies, which generally contribute less information to the image, are discarded The effectiveness of a compression technique depends upon the type of data Figure 17 is a photograph and Figure 18 is a drawing The photograph contains many areas with small changes in color, while the drawing has large areas with the same color
Figure 17 IRENEBMP
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Figure 18 STARSBMP
Figure 19 shows the relative file sizes when the photograph in Figure 17 is compressed using various formats: Uncompressed BMP, BMP with RLE encoding, GIF, PNG, and JPEG Notice that BMP-RLE and GIF produce very little compression while PNG and, especially, JPEG produce significant reductions in file size Figure 110 contains a similar graph, this time using the drawing in Figure 18 You can see that the other file formats nearly catch up to JPEG with this image Lossless versus Lossy Compression Most image file formats use what is known as lossless compression By this we mean that if we take an image, compress it using a lossless technique, and expand it again, the resulting image is bit-by-bit identical to the original Some compression methods (notably JPEG) are lossy Using the compression sequence described above, lossy compression produces an image that is
Figure 19 Percent of Original Size Compressing IRENEBMP
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Figure 110 Percent of Original Size Compressing STARSBMP
close to the original but not an exact match That is, a pixel with an RGB color value of (128,243,118) in an image that is compressed may produce (127,243,119) when it is expanded In image compression, lossy techniques take advantage of the fact that the eye has a hard time distinguishing between nearly identical colors The reason for using lossy compression is that it generally gives significantly greater compression than lossless methods do In many situations, small losses of data are acceptable in exchange for increased compression
Byte and Bit Ordering All bitmap image files contain integers stored in binary format For single-byte integers there is no compatibility problem among different processor types This is not the case with multi-byte integers When reading multi-byte integers there is the issue of how to order the bytes from the input stream in the integer Suppose that an image file contains these two bytes in succession 0 1 1 0 0 0 1 1 (6316) 0 0 0 1 1 1 0 1 (1D16) Most Significant Bit Least Significant Bit
If these bytes represent a 2-byte integer should they be interpreted as 0 0 0 1 1 1 0 1 0 1 1 0 0 0 1 1 (1D6316) 75,2310
0 1 1 0 0 0 1 1 0 0 0 1 1 1 0 1 (631D16) 25,37310 In other words, in a multi-byte integer does the most significant byte occur first or last in the file