Abstract
In this chapter we examine compression algorithms such that recovered input data cannot be exactly reconstructed from compressed version. This termed “loss”. What we have, then, is a tradeoff between efficient compression versus a less accurate version of the input data. This tradeoff is captured in the Rate-Distortion Theory. Most of the loss occurs in quantization, and we introduce both Uniform and Nonuniform Scalar Quantization, and then Vector Quantization. Transform Coding, especially the Discrete Cosine Transform (DCT), is the main step in JPEG compression. We study DCT in great length and provide several examples. A newer version, JPEG2000, is supported by Wavelet-Based Coding so we introduce this method here and go on to study Wavelet Packets, the Embedded Zerotree of Wavelet Coefficients, and SPIHT.
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© 2014 Springer International Publishing Switzerland
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Li, ZN., Drew, M.S., Liu, J. (2014). Lossy Compression Algorithms. In: Fundamentals of Multimedia. Texts in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-05290-8_8
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DOI: https://doi.org/10.1007/978-3-319-05290-8_8
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