... and the likely drawbacks are reviewed. Keywords: Machine Learning Image Compression, DCT, Genetic Algorithm, Fractal. Encoding, Chroma Subsampling ...
IMAGE COMPRESSION USING MACHINE LEARNING TECHNIQUES ABSTRACT This review focuses on research on the combination of traditional compression algorithms and machine learning techniques. Both compression and learning algorithms are discussed, providing background and reasoning for their combination. It provides comparisons between different techniques DCT, Fractal encoding, Genetic Algorithms, Neural Networks, and their combinations, using different measures of image fidelity, compression ratio, and, compression and decompression efficiency. The results provide evidence to the significant improvement of compression rates when using the discussed techniques, and the potential applications and the likely drawbacks are reviewed. Keywords: Machine Learning Image Compression, DCT, Genetic Algorithm, Fractal Encoding, Chroma Subsampling, Neural Network.
TABLE OF CONTENTS Abstract ................................................................................................................................. 1 Introduction ........................................................................................................................ 3 Traditional image compression algorithms ..................................................................... 3 Transform coding: Discrete Cosine Transform (DCT) ............................................................. 3 Fractal encoding ....................................................................................................................................... 5 Chroma subsampling .............................................................................................................................. 6 Applicable machine learning techniques .......................................................................... 6 Artificial Neural Networks ................................................................................................................... 6 Genetic Algorithms .................................................................................................................................. 7 Discussion ............................................................................................................................ 7 Combining learning and compression algorithms ......................................................... 7 Genetic algorithms ................................................................................................................................... 8 Genetic algorithms and DCT ........................................................................................................................... 8 Genetic algorithms and fractal encoding .................................................................................................. 8 Neural networks .................................................................................................................................... 10 Neural networks and DCT ............................................................................................................................ 10 Neural networks and fractal encoding .................................................................................................... 11 Evaluation ......................................................................................................................... 11 Conclusion ......................................................................................................................... 12 Glossary: ............................................................................................................................ 13 Bibliography ..................................................................................................................... 13
INTRODUCTION Natural images tend to contain a high number of redundant information; neighbouring pixels tend to exhibit a correlation (Kinsner, 2002). Most image compression algorithms tend to take advantage of this correlation to reduce the size of images stored. This article will review the current state of image compression using machine-learning paradigms, to achieve that; it will first explore traditional compression algorithms, machine learning techniques applicable, and forefront research involving the combination of both.
TRADITIONAL IMAGE COMPRESSION ALGORITHMS There are a few techniques that can be used for machine learning, in the following section they will be explained. TRANSFORM CODING: DISCRETE COSINE TRANSFORM (DCT) Discrete Cosine Transforms are the base algorithm for JPEG compression, one of the most widely used image compression algorithms (Luo et al, 2010). It works by dividing an image into squares and applying a discrete cosine function to the resulting matrix of values, hence representing the image as a superposition of wavelets rather than pixel values (Caroline et al, 2010). The example on illustrates the transformation process. Figure 1 shows an example image in black and white, within a colour space of 255, its matrix representation is displayed in Table 1, with 0 being black, and 255 white pixels. FIGURE 1
TABLE 1
If we apply Equation 1 to the matrix displayed in Table 1 we will obtain a representation of our 8x8 grid by superimposing the wavelets shown in Figure 2. This process is similar to a two-dimensional Fourier transform, the main difference being the use of cosines exclusively, and the Gamma Function (Equation 2) that provides a discrete solution, instead of an infinite continuous one.
EQUATION 1 )
𝐹 𝑢, 𝑣 =
)
' * ' * ( +
(89 =;
