What is recorded here?
For two finite-alphabet iid sources, separate encoders and a joint decoder can approach the correlated-source entropy rate region, even though neither encoder sees the other input. Imagine two instruments producing related streams. If each compresses in isolation using only its own stream statistics, it can miss the redundancy shared between them. Slepian and Wolf show that separate encoders can still exploit that redundancy when the decoder has both messages. The proof assigns blocks to randomly chosen code collections; the decoder uses the other stream to distinguish possibilities. Their result determines an asymptotic rate region and shows why independent access at the encoders need not require two independent compression budgets. It establishes existence under a precise model, leaving efficient finite-length code design and more general sources for further work.
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Scientific scope
Two finite-alphabet sources formed by iid draws of a fixed joint distribution; each encoder sees only its own block and a joint decoder receives both noiseless messages. The region is the closure of achievable rate pairs: Rx>=H(X|Y), Ry>=H(Y|X), Rx+Ry>=H(X, Y), in a consistent log base. Achievability means block error probability can be made arbitrarily small with increasing block length/rate slack; closure does not promise finite-block zero error at every boundary. Random-supercode existence and time sharing do not provide an efficient implementable code or finite resource guarantee. General ergodic sources, continuous alphabets, rate-distortion, many-source variants and variable-length design are expressly left open in this paper. The target is the paper on pages 471-480. This account does not supply a machine-checked proof or independently certified numerical and formula transcription. Neighboring article fragments are excluded. No practical compression benchmark, real communication reliability or later noisy-channel results absorbed.
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People and systems
David Slepian; Jack K. Wolf
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English catalogue narrative
Slepian and Wolf: compressing correlated data separately
For two finite-alphabet iid sources, separate encoders and a joint decoder can approach the correlated-source entropy rate region, even though neither encoder sees the other input.
Imagine two instruments producing related streams. If each compresses in isolation using only its own stream statistics, it can miss the redundancy shared between them. Slepian and Wolf show that separate encoders can still exploit that redundancy when the decoder has both messages. The proof assigns blocks to randomly chosen code collections; the decoder uses the other stream to distinguish possibilities. Their result determines an asymptotic rate region and shows why independent access at the encoders need not require two independent compression budgets. It establishes existence under a precise model, leaving efficient finite-length code design and more general sources for further work.
Scientific scope
Two finite-alphabet sources formed by iid draws of a fixed joint distribution; each encoder sees only its own block and a joint decoder receives both noiseless messages. The region is the closure of achievable rate pairs: Rx>=H(X|Y), Ry>=H(Y|X), Rx+Ry>=H(X, Y), in a consistent log base. Achievability means block error probability can be made arbitrarily small with increasing block length/rate slack; closure does not promise finite-block zero error at every boundary. Random-supercode existence and time sharing do not provide an efficient implementable code or finite resource guarantee. General ergodic sources, continuous alphabets, rate-distortion, many-source variants and variable-length design are expressly left open in this paper. The target is the paper on pages 471-480. This account does not supply a machine-checked proof or independently certified numerical and formula transcription. Neighboring article fragments are excluded. No practical compression benchmark, real communication reliability or later noisy-channel results absorbed.
People and systems
David Slepian; Jack K. Wolf