Compression of Black-White Images with Arithmetic Coding

Langdon, Glen G.; Rissanen, Jorma

doi:10.1109/tcom.1981.1095052

Cited by 321 publications

(127 citation statements)

References 8 publications

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“…In the basic algorithm outlined in Section 1 and in the work of Witten, Neal, and Cleary, u p t o t w o m ultiplications and one division are needed for each symbol encoded. Work by Rissanen, Langdon, Mohiuddin, and others at IBM 5,16,18,22,27 eliminates the division altogether and focuses on approximating the multiplication by combinations of additions and shifts. In 13 we present an alternative approach i n which w e approximate an arithmetic coder by a nite state automaton with a small number of states.…”

Section: Resultsmentioning

confidence: 99%

Analysis of arithmetic coding for data compression

Howard

Vitter

1992

Information Processing & Management

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Arithmetic coding, in conjunction with a suitable probabilistic model, can provide nearly optimal data compression. In this article we analyze the e ect that the model and the particular implementation of arithmetic coding have on the code length obtained. Periodic scaling is often used in arithmetic coding implementations to reduce time and storage requirements; it also introduces a recency e ect which can further a ect compression. Our main contribution is introducing the concept of weighted entropy and using it to characterize in an elegant w a y the e ect that periodic scaling has on the code length. We explain why and by h o w m uch scaling increases the code length for les with a homogeneous distribution of symbols, and we c haracterize the reduction in code length due to scaling for les exhibiting locality of reference. We also give a rigorous proof that the coding e ects of rounding scaled weights, using integer arithmetic, and encoding end-of-le are negligible.

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Section: Resultsmentioning

confidence: 99%

Analysis of arithmetic coding for data compression

Howard

Vitter

1992

Information Processing & Management

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“…A well-known approach is arithmetic coding (here in short, AC) [15] that widely compresses multimedia data such as JPEG2000 and H.264 video coding [16][17][18][19]. Hardware implementations are proposed such as [20,21] to accelerate the computing speed.…”

Section: Related Workmentioning

confidence: 99%

Lazy Management for Frequency Table on Hardware-Based Stream Lossless Data Compression

et al. 2016

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Abstract:The demand for communicating large amounts of data in real-time has raised new challenges with implementing high-speed communication paths for high definition video and sensory data. It requires the implementation of high speed data paths based on hardware. Implementation difficulties have to be addressed by applying new techniques based on data-oriented algorithms. This paper focuses on a solution for this problem by applying a lossless data compression mechanism on the communication data path. The new lossless data compression mechanism, called LCA-DLT, provides dynamic histogram management for symbol lookup tables used in the compression and the decompression operations. When the histogram memory is fully used, the management algorithm needs to find the least used entries and invalidate these entries. The invalidation operations cause the blocking of the compression and the decompression data stream. This paper proposes novel techniques to eliminate blocking by introducing a dynamic invalidation mechanism, which allows achievement of a high throughput data compression.

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“…Both the encoder and decoder of a PPM system, adapts the model of coding dynamically to the message statistics as the transmission proceeds (Langdon and Rissanen, 1981). Adaptive coding is effective because what is happening is counted and used as the basis of subsequent coding so that the counts only needs to be incremented in the event that a character is correctly predicted (Moffat, 1990).…”

Section: Ppm Implementationmentioning

confidence: 99%

A Single Core Hardware Module of a Data Compression Scheme Using Prediction by Partial Matching Technique

Jalil

Mamun²,

Marufuzzaman

et al. 2011

American Journal of Applied Sciences

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Problem statement:Compression is useful because it helps reduce the consumption of expensive resources, such as hard disk space or transmission bandwidth. For effective data compression, the compression algorithm must be able to predict future data accurately in order to build a good probabilistic model for compression. Lossless compression is essential in cases where it is important that the original and the decompressed data be identical, or where deviations from the original data could be deleterious. Approach: Prediction by Partial Matching (PPM) data compression technique had utmost performance standard and capable of very good compression on a variety of data. In this research, we had introduced PPM technique to compress the data and implemented the algorithm on Altera FLEX10K FPGA device that allows for efficient hardware implementation. The PPM algorithm was modeled using the hardware description language VHDL. Results: Functional simulations were commenced to verify the functionality of the system with both 16-bit input and 32-bit input. The FPGA utilized 1164 logic cells with a maximum system frequency of 95.3MHz on Altera FLEX10K. Conclusion: The proposed approach is computationally simple, accurate and exhibits a good balance of flexibility, speed, size and design cycle time.

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Compression of Black-White Images with Arithmetic Coding

Cited by 321 publications

References 8 publications

Analysis of arithmetic coding for data compression

Analysis of arithmetic coding for data compression

Lazy Management for Frequency Table on Hardware-Based Stream Lossless Data Compression

A Single Core Hardware Module of a Data Compression Scheme Using Prediction by Partial Matching Technique

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