A VLSI chip set for high-speed lossless data compression

Abstract. We approach the theoretical problem of compressing a signal dominated by Gaussian noise. We present expressions for the compression ratio which can be reached, under the light of Shannon's noiseless coding theorem, for a linearly quantized stochastic Gaussian signal (noise). The compression ratio decreases logarithmically with the amplitude of the frequency spectrum P (f ) of the noise. Entropy values and compression rates are shown to depend on the shape of this power spectrum, given different normalizations. The cases of white noise (w.n.), f np power-law noise -including 1/f noise-, (w.n.+1/f ) noise, and piecewise (w.n.+1/f | w.n.+1/f 2 ) noise are discussed, while quantitative behaviours and useful approximations are provided.

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“…h ≡ h cont = log 2 √ 2πe σ , (C. 13) in agreement with eq. (3.3) in the limit of small ∆η, as expected from the comments in the previous section.…”

Section: C1 Entropy In the Continuous Casesupporting

confidence: 70%

“…For one dimension, the Huffman scheme is known to be reasonably close 1 (see also the performance of other methods such as the Rice algorithm in [13]). Thus, the compression ratio will satisfy c r ≃…”

Section: The Basic Data Compression Problemmentioning

confidence: 99%

Information Content in Uniformly Discretized Gaussian Noise: Optimal Compression Rates

Romeo

Gaztañaga

Barriga

et al. 1999

Int. J. Mod. Phys. C

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“…In [1] a single-cycle fully digital implementation of the Golomb-Rice encoder required 34000 transistors. Considering an average of 4 transistors per gate, the fully-digital implementation requires 8500 gates.…”

Section: Hardware Implementationmentioning

confidence: 99%

“…Compression is achieved in the entropy encoder by assigning short codewords to frequent samples and long codewords to less frequent samples. Due to the high complexity of entropy encoders such as the Huffman or the arithmetic encoders, the entropy coding stage is usually implemented using a dedicated ASIC or a microprocessor [1].…”

Section: Introductionmentioning

confidence: 99%

Low-Complexity Compression for Sensory Systems

Leon-Salas

2015

IEEE Trans. Circuits Syst. II

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This paper presents a low-complexity mixed-domain data compression solution suitable for resource-constrained wireless sensory systems. Data compression reduces the transmission bandwidth of sensor nodes helping them to save energy and extend their operation time. In the proposed compression solution, the sensor signal is decorrelated in the analog domain and converted to digital using a compressing analogto-digital converter. The compressing converter is based on a cyclic converter architecture and is able to jointly perform the functions of quantization, signal conversion and entropy coding in a single circuit. Since data compression is performed as the sensor signal is acquired, there is no need to use a microprocessor or a dedicated circuit to compress the signal reducing the computational requirements of a sensor node. As a proof of concept the proposed data compression scheme was implemented using programmable hardware and employed to acquire and compress physiological and speech signals.

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“…The output of the source is a set of symbols with a given probability of occurrence. Hardware implementations of popular compression algorithms such as the Huffman coding [1], Lempel-Ziv coding [2], binary arithmetic coding [3], and the Rice algorithm [4] have been reported in the literature. These complex hardware implementations typically preclude the direct integration of an A/D and a compression algorithm at the sensor level.…”

Section: Introductionmentioning

confidence: 99%

An Analog-to-Digital Converter with Golomb-Rice Output Codes

Leon

Balkir

Sayood

et al.

2005 IEEE International Symposium on Circuits and Systems

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An analog-to-digital converter with data compression capabilities is described. By sharing circuits between an integrating converter and a Golomb-Rice encoder it is possible to jointly perform the tasks of quantization and coding. The Golomb-Rice codes are generated during the conversion cycle by employing a shift register and a digital multiplexer. The final codeword is read out serially from the shift register. The converter can also work in a non-compressing mode. This design provides a compact circuit suitable for on-sensor compression. Simulations at the system and transistor level corroborate the validity of the design.

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A VLSI chip set for high-speed lossless data compression

Cited by 30 publications

References 7 publications

Information Content in Uniformly Discretized Gaussian Noise: Optimal Compression Rates

Information Content in Uniformly Discretized Gaussian Noise: Optimal Compression Rates

Low-Complexity Compression for Sensory Systems

An Analog-to-Digital Converter with Golomb-Rice Output Codes

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