LIGN 17

LIGN 17 - • Consider a stereotypical random event coin-flipping o If we had a sequence of 1000 coin flips and we saw 12 heads in a row would that

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Gwen’s Lecture 02/25/11 19:01 Recall Lossless v. Lossy Compression o Lossless – we can recover all of the data in original msg, even tho we  made it smaller Huffman coding o Lossy – we can’t recover all data in original msg We threw away some bits b/c the human perceptual system doesn’t  need them anyway Benefit: achieve better compression ratio Mp3, jpeg formats Compression Artifacts Compression artifacts happen because of overcompression o Jpeg: “busy” parts of image gets pixelated/blurred o Mp3: random noise like clapping gets tiny ring Why? o Randomness is unpredictable – contains more info than we can compress  out of it That’s Random… What’s going to be problematic abt compressing randomness?
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Unformatted text preview: • Consider a stereotypical random event: coin-flipping o If we had a sequence of 1000 coin flips, and we saw 12 heads in a row, would that be random? o It turns out that the odds of not seeing such a streak during that whole sequence are less than 1 in 11 Does that pattern, or any other pattern show up more or less freq than it should? o How would you know if a coin were fair? What would the frequency table look like? Spectral Test for Randomness • Looks at dist over sequences of heads and tails to see if they could have arisen by any chance 19:01 19:01...
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This note was uploaded on 08/30/2011 for the course LIGN 17 taught by Professor Kehler during the Winter '08 term at UCSD.

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LIGN 17 - • Consider a stereotypical random event coin-flipping o If we had a sequence of 1000 coin flips and we saw 12 heads in a row would that

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