Hence, after decoding, the data can be restored to its original state and there is no reduction in quality. Lossless encoding keeps the bytes which are non-noticeable while compressing data. It is hard to determine the compression ratio that can be achieved using Fractal Compression as the image can be decoded at any scale. The algorithm converts these parts into fractal codes, which can then be used to recreate the entire image. Hence storing the image as a collection of transformations could be useful in compressing the data. Fractal Compressionįractal compression uses the fact that different parts of an image are similar that is, images can be represented as fractals. Discrete Cosine Transform makes image transmission possible over networks. One of the ways to calculate DCT is by using Fourier transformation. A large amount of information is stored in a very low-frequency component of a signal. The data is broken into various frequencies. Some of the most famous algorithms for lossy compression are: 1. Original image and image after lossy compression Algorithms for lossy compression Zoom into the following image to notice the difference. Lossy encoding generally results in a high degree of compression.Īpplication: It is used for compressing audios and videos. There is also a loss in the quality of the data since some bits are lost forever. However, when decoded, it cannot be restored into its initial state. This process reduces the actual data size. In the case of lossy encoding, data that is not noticeable is removed. We will go through each of the methods in detail now. It is also used in application processing for file conversions.įollowing are the types of data encoding algorithms: Encoding is used for data transmissions, data storage, and data compression. Data Encoding is the process of conversion of data in a form suitable for various types of information processing.
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