The Use of Asymmetric Numeral Systems Entropy Encoding in Video Compression

Konstantinov, Fedor V.; Gryzov, Gennady Yu.; Bystrov, Kirill

doi:10.1007/978-3-030-36614-8_10

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…One of the main entropy coding types creates and assigns every single symbol of the entry into a unique prefix-free code. There are more than 16 algorithms support entropy algorithms such as Arithmetic Coding [ 71 , 72 , 73 , 74 ], Asymmetric Numeral Systems (ANS) [ 75 , 76 , 77 ], Golomb Coding [ 78 , 79 ], Adaptive Huffman [ 80 , 81 , 82 ], Canonical Huffman [ 83 ], Modified Huffman [ 84 ], Range encoding [ 85 , 86 ], Shannon [ 87 ], Shannon–Fano [ 88 , 89 , 90 ], Shannon–Fano–Elias [ 91 ], Tunstall coding [ 92 , 93 ], Unary coding [ 94 , 95 , 96 ], Universal Exp-Golomb [ 97 , 98 ], Universal Fibonacci Coding [ 99 , 100 , 101 ], Universal Gamma Coding [ 102 , 103 ], Universal Levenshtein Coding [ 104 ].…”

Section: Compressionmentioning

confidence: 99%

The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on IoT Nodes in Smart Cities

Nasif

Othman

Sani

2021

Sensors

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Networking is crucial for smart city projects nowadays, as it offers an environment where people and things are connected. This paper presents a chronology of factors on the development of smart cities, including IoT technologies as network infrastructure. Increasing IoT nodes leads to increasing data flow, which is a potential source of failure for IoT networks. The biggest challenge of IoT networks is that the IoT may have insufficient memory to handle all transaction data within the IoT network. We aim in this paper to propose a potential compression method for reducing IoT network data traffic. Therefore, we investigate various lossless compression algorithms, such as entropy or dictionary-based algorithms, and general compression methods to determine which algorithm or method adheres to the IoT specifications. Furthermore, this study conducts compression experiments using entropy (Huffman, Adaptive Huffman) and Dictionary (LZ77, LZ78) as well as five different types of datasets of the IoT data traffic. Though the above algorithms can alleviate the IoT data traffic, adaptive Huffman gave the best compression algorithm. Therefore, in this paper, we aim to propose a conceptual compression method for IoT data traffic by improving an adaptive Huffman based on deep learning concepts using weights, pruning, and pooling in the neural network. The proposed algorithm is believed to obtain a better compression ratio. Additionally, in this paper, we also discuss the challenges of applying the proposed algorithm to IoT data compression due to the limitations of IoT memory and IoT processor, which later it can be implemented in IoT networks.

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Section: Compressionmentioning

confidence: 99%

The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on IoT Nodes in Smart Cities

Nasif

Othman

Sani

2021

Sensors

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“…Some ANS encoders and decoders have been shown to allow for very fast software implementations in modern CPUs [4], [5]. This has lead to its incorporation in recent data compression standards and its use in many different cases [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19]. In addition, ANS-based encoders could be applicable to a very wide range of multimedia scenarios, such as an alternative to the Rice-Golomb codes employed in the energy-efficient scheme described in [20], as a high-throughput entropy encoder in a high frame rate video format [21], or in general as an entropy encoder in schemes for sparse coding [22], learned image compression [23], compressive sensing [24], or point cloud data compression [25].…”

Section: Introductionmentioning

confidence: 99%

Redundancy and Optimization of tANS Entropy Encoders

Blanes¹,

Hernández-Cabronero²,

Serra-Sagristà³

et al. 2021

IEEE Trans. Multimedia

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Nowadays entropy encoders are part of almost all data compression methods, with the Asymmetrical Numeral Systems (ANS) family of entropy encoders having recently risen in popularity. Entropy encoders based on the tabled variant of ANS are known to provide varying performances depending on their internal design. In this paper, we present a method that calculates encoder redundancies in almost linear time, which translates in practice to thousand-fold speedups in redundancy calculations for small automatons, and allows redundancy calculations for automatons with tens of millions of states that would be otherwise prohibitive. We also address the problem of improving tabled ANS encoder designs, by employing the aforementioned redundancy calculation method in conjunction with a stochastic hill climbing strategy. The proposed approach consistently outperforms state-of-the-art methods in tabled ANS encoder design. For automatons of twice the alphabet size, experimental results show redundancy reductions around 10% over the default initialization method and over 30% for random initialization.

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A simplified variant of tabled asymmetric numeral systems with a smaller look-up table

Wang

Lin

2020

Distrib Parallel Databases

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The Use of Asymmetric Numeral Systems Entropy Encoding in Video Compression

Cited by 4 publications

References 5 publications

The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on IoT Nodes in Smart Cities

The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on IoT Nodes in Smart Cities

Redundancy and Optimization of tANS Entropy Encoders

A simplified variant of tabled asymmetric numeral systems with a smaller look-up table

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