The Improvement with Golomb Code and JPEG2000 in Image Compression
Date Issued
2012
Date
2012
Author(s)
Pan, Guan-Chen
Abstract
With the advancement of the Internet and the multimedia, the demand of people in image and video becomes higher and higher. In the past days, the requirements may be just a clear photo or a smooth video, but people did not satisfy with that. So the high quality image and video have been come out, such as high resolution pictures, high definition (HD) video, and full high definition (full HD) video. Because the size of multimedia data becomes higher, so people need to find some new ways to deal with the high data size of the multimedia. To solve the problem, there are some of the compression techniques. The most well-known image compression technique is Joint Photographic Experts Group 0[2][3], which is also called JPEG. JPEG is the most popular standard in image compression and still have been widely used in the worldwide nowadays.
The Huffman coding is used in JPEG, and it is the most famous entropy coding method and widely used in many images and video coding standards. Nevertheless, Huffman coding cannot be used if the source is ideally geometrically distributed because the number of elements is infinite. But Golomb coding can do well when the source is ideally geometrically distributed. Golomb coding is a good entropy coding method when the data source is geometric distribution, and it does not need coding table, but Huffman coding does. Nonetheless when the input data is not geometric distribution, the Golomb coding may not be a good choice. Moreover, the input data may be positive and negative numbers, but the Golomb coding is only for positive numbers. To solve the problem, we proposed the modified Golomb coding with asymmetric two-sided geometric distributed data. It can be used in many ways, and can have better performance.
In addition, there are still some famous image compression standards, such as JPEG2000, SPIHT, and …etc. Some of them have better performance in compression than that of JPEG. JPEG2000 [4][7][8] is another worldwide image compression standard, and can have better compression ratio and image quality than JPEG does. The arithmetic coding is used in the encoder of JPEG2000, and its probability table is fixed. Because record every probability table needs lots of storage space, it may be not efficient to record every probability table and the compression ratio may be worse. So the JPEG2000 standard used a fixed probability table to deal with everything, and still have good performance. To improve this part, we proposed a new kind of arithmetic coding, which can use arithmetic coding with different probability table, but still have better compression ratio than that of JPEG2000.
The buffer size of the image compression standard is still another problem. Due to the size of mobile systems, such as digital cameras and cell phones, is becoming smaller today. The storage space of those systems may be more precious. Using the same buffer size to have the better image quality may be another good topic for image compression. To deal with this topic, we proposed a new method, which is combined discrete cosine transform and discrete wavelet transform. Compare to the JPEG standard, our method need the same buffer size as JPEG, but have better performance than that of JPEG.
Subjects
Image Coding
Huffman coding
Golomb coding
Asymmetric
Geometric distribution
Arithmetic coding
Probability table
Buffer size
Type
thesis
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