L19_perceptual_coding

L19_perceptual_coding - 6.02 Spring 2008 Perceptual Coding,...

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Unformatted text preview: 6.02 Spring 2008 Perceptual Coding, Slide 1 Perceptual Coding • Lossless vs. lossy compression • Perceptual models • Selecting info to eliminate • Quantization and entropy encoding • Part II wrap up 6.02 Spring 2008 Perceptual Coding, Slide 2 Lossless vs. Lossy Compression • Huffman and LZW encodings are lossless , i.e., we can reconstruct the original bit stream exactly: bits OUT = bits IN . – What we want for “naturally digital” bit streams (documents, messages, datasets, …) • Any use for lossy encodings: bits OUT ! bits IN ? – “Essential” information preserved – Appropriate for sampled bit streams (audio, video) intended for human consumption via imperfect sensors (ears, eyes). Source Encoding Source Decoding Store/Retrieve Transmit/Receive bits IN bits OUT 6.02 Spring 2008 Perceptual Coding, Slide 3 Perceptual Coding • Start by evaluating input response of bitstream consumer (eg, human ears or eyes), i.e., how consumer will perceive the input. – Frequency range, amplitude sensitivity, color response, … – Masking effects • Identify information that can be removed from bit stream without perceived effect, e.g., – Sounds outside frequency range, or masked sounds – Visual detail below resolution limit (color, spatial detail) – Info beyond maximum allowed output bit rate • Encode remaining information efficiently – Use DCT-based transformations – Quantize DCT coefficients – Entropy code (eg, Huffman encoding) results 6.02 Spring 2008 Perceptual Coding, Slide 4 Perceptual Coding Example: Images • Characteristics of our visual system ! opportunities to remove information from the...
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This note was uploaded on 08/23/2009 for the course EECS 6.02 taught by Professor Terman during the Spring '08 term at MIT.

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L19_perceptual_coding - 6.02 Spring 2008 Perceptual Coding,...

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