JPEG - Idea and Practice/The compression and encoding

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The compression of the file[edit | edit source]

For each sxs-square and for each of the three YCbCr coordinates (or components) we have, after the cosine transform and the quantization, a sequence of s2 integers ordered after the zigzag principle. In each of these sequences we have replaced the first number - the DC term - by its derivation from the preceding DC term (that of the preceding sxs-square and the same component). However, because most of these integers (when the square runs through the picture) are usually zero, it is expedient to introduce them into the file in a certain way, namely by letting every second integer be a true number and every other integer be a number of zeros (in an unbroken chain). The integers (in the new sequence) can be negative and of any size, and it is now our task to convert the integers to sequences of bits that are as short as possible. As a file consists of bytes, we must hereafter divide the resulting stream of bits into 8-blocks and convert these to bytes.

Since the integers are allowed to be of any size, we must express each integer as a pair of two sequences of bits, the first being a sequence which in some way (possibly in a coded form) corresponds to a natural number stating the length of the second sequence, which is the binary digit expression of the number in question. The first sequence of bits could simply be the binary expression of the natural number, but then these sequences would have to have the same length, for instance 4. As 4 bits can express natural numbers from 1 to 16, and since by using no more than 16 bits we can express integers up to 216-1 = 65535, this method can be used for a picture which is fairly varied in colours or which is not too large. If you write a JPEG program, you should begin with this method, and first introduce one of those described below when the program works, because it is a simple method which can compress an appropriate photo to 15 percent of its size in BMP. But the 4 bits must be extended to 5, if the program is to be able to handle all sorts of pictures, and even 4 bits are too many bits to spend on stating these lengths, since most of the lengths are rather short. It would be preferable if we had a method that allowed the length of the first sequences (of the pairs) to vary.

Our numbers (stating numbers of bits) are natural numbers, and we want to represent them by sequences of bits in such a way that the most frequently used numbers correspond to the shortest sequences, and we must have a method that makes us able to determine when a sequence terminates. The first description of a principle that can put the elements of a given set (in our case the set of the natural numbers) into a one-to-one correspondence with sequences of bits, so that the length of a sequence is inverse proportional to the frequency of use of the element, is Shannon and Fano's method of coding from 1949.

The coding of Shannon and Fano[edit | edit source]

Assume that we have a procedure the result of which is a long reeling-off of information, which is expressed by using the elements of a given set. We want this set replaced by a set consisting of sequences of bits, in such a way that the most used sequences are the shortest. To this end, you can do the following: divide the set up in two parts so that the elements in each part are used with approximately the same frequency. For the elements in the first part, let the sequences begin with 0, and for the elements in the second part, let the sequences begin with 1. Divide each of these two sets up in two parts, so that the elements in each part are used with approximately the same frequency, and let the next bit be 0 for the elements in the first parts, and 1 for the elements in the second parts, and so on.

In our case the set in question is the set of natural numbers, and the meaning of such a number is that it states the length of the binary digit expression of an integer. The frequencies of use of the natural numbers are in some way inverse proportional to their size, and we ought to theorize about the frequencies, or test a number of random pictures and take average values. However, in this case we will only make a guess determined by our desire to get a simple formula: we assume that (the elements of) {1, 2, 3} come with the same frequency as the rest, that {1} comes with the same frequency as {2, 3}, that {4, 5} come with the same frequency as {6, 7, ...}, that {6, 7} come with the same frequency as {8, 9, ...}, and so on. With these assumptions, coding of the natural numbers will look like this:

1        00
2        010
3        011
4        100
5        101
6        1100
7        1101
8        11100
9        11101
10      111100
11      111101
12      1111100
13      1111101

Note that for n larger than 3, the number of 1's before the first 0 is the whole part of n/2 minus 1, and after this 0, there is only one bit more: 0 for n even and 1 for n odd. When (in the stream of bits) we know that some of the following bits form such a block of bits, we can easily determine when it terminates, as well as determine the corresponding natural number: if the first bit is 0, a bit more will follow; if this is 0, the number is 1, if it is 1, a bit more will follow; if this is 0, the number is 2, if it is 1, the number is 3. If the first bit is 1, we count the number of 1's before the first 0, and we know that the sequence terminates just after this 0. We add 1 to the number of the 1's and multiply this number by 2. The natural number, then, is this number, if the last bit is 0, and the succeeding number, if the last bit is 1.

The integers that are the result of the cosine transform and the quantization (s2 integers for each sxs-square and each component), when the squares run through the picture, have been written in a certain way, namely so that every second integer is a true number and every other integer states a number (possibly zero) of zeros. Furthermore, we have written these integers as sequences of bits each having two parts: the first part is written in a coded form and corresponds to a natural number the purpose of which is to state the number of bits in the second part, being the binary digit expression of the integer in question. But since the integers (of the "true" type) can be negative, we must indicate this in some way. You probably think that we have to use an extra bit for this, however this is not necessary: the first digit of the digit expression (being the most significant digit) will always be 1, and we can indicate that the number is negative by replacing this 1 by a 0. The resulting stream of bits is ultimately divided up in 8-blocks, which are written into the file as bytes - possibly extending the very last block (by 0's or by 1's) so that it becomes an 8-block.

We have used this simple method of coding in our demonstration program, and as it can compress a well suited photo to 6-12 per cent of its original size, we cannot here see any reason for choosing a method involving more machinery. Nonetheless, we will now say a little about the method of coding used by JPEG (and explained in details in part two).

The coding of Huffman[edit | edit source]

If we had spent more time studying frequencies, we could have got a more efficient program. However, the method of Shannon and Fano is not the best method. The most efficient method of coding is that of Huffman, invented in 1951. This method has been almost universal in the JPEG procedure. We will describe it in part two, and the reader will understand why we have avoided it here: it is not easy to describe and illustrate, and the encoding and the decoding demand more operations. Besides, in the JPEG procedure the DC numbers and the AC numbers are Huffman-coded in a different way, and the Y component and the colour components use different Huffman tables.

The coding method of Huffman can be proved to be the most efficient one, but this superiority presupposes that all the data are encoded in the same way, and this is not the case in the JPEG compression. Therefore, the JPEG committee prescribed, besides the Huffman coding, the so-called arithmetic coding, which can compress pictures a little bit more. However, the arithmetic coding is slower and it has not been used much - partly because it has been patented.