Adaptive Vector Quantization for Lossy Compression of Image Sequences
In this work, we present a scheme for the lossy compression of image sequences, based on the Adaptive Vector Quantization (AVQ) algorithm. The AVQ algorithm is a lossy compression algorithm for grayscale images, which processes the input data in a single-pass, by using the properties of the vector q...
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MDPI AG
2017-05-01
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Series: | Algorithms |
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Online Access: | http://www.mdpi.com/1999-4893/10/2/51 |
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author | Raffaele Pizzolante Bruno Carpentieri Sergio De Agostino |
author_facet | Raffaele Pizzolante Bruno Carpentieri Sergio De Agostino |
author_sort | Raffaele Pizzolante |
collection | DOAJ |
description | In this work, we present a scheme for the lossy compression of image sequences, based on the Adaptive Vector Quantization (AVQ) algorithm. The AVQ algorithm is a lossy compression algorithm for grayscale images, which processes the input data in a single-pass, by using the properties of the vector quantization to approximate data. First, we review the key aspects of the AVQ algorithm and, subsequently, we outline the basic concepts and the design choices behind the proposed scheme. Finally, we report the experimental results, which highlight an improvement in compression performances when our scheme is compared with the AVQ algorithm. |
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format | Article |
id | doaj.art-ee795971226040aaa8f9465c7f91e981 |
institution | Directory Open Access Journal |
issn | 1999-4893 |
language | English |
last_indexed | 2024-12-23T06:26:08Z |
publishDate | 2017-05-01 |
publisher | MDPI AG |
record_format | Article |
series | Algorithms |
spelling | doaj.art-ee795971226040aaa8f9465c7f91e9812022-12-21T17:57:04ZengMDPI AGAlgorithms1999-48932017-05-011025110.3390/a10020051a10020051Adaptive Vector Quantization for Lossy Compression of Image SequencesRaffaele Pizzolante0Bruno Carpentieri1Sergio De Agostino2Dipartimento di Informatica, Università di Salerno, Via Giovanni Paolo II, 132, Fisciano, SA 84084, ItalyDipartimento di Informatica, Università di Salerno, Via Giovanni Paolo II, 132, Fisciano, SA 84084, ItalyComputer Science Department, Sapienza University, Via Salaria 113, Rome 00185, ItalyIn this work, we present a scheme for the lossy compression of image sequences, based on the Adaptive Vector Quantization (AVQ) algorithm. The AVQ algorithm is a lossy compression algorithm for grayscale images, which processes the input data in a single-pass, by using the properties of the vector quantization to approximate data. First, we review the key aspects of the AVQ algorithm and, subsequently, we outline the basic concepts and the design choices behind the proposed scheme. Finally, we report the experimental results, which highlight an improvement in compression performances when our scheme is compared with the AVQ algorithm.http://www.mdpi.com/1999-4893/10/2/51lossy compressionadaptive vector quantizationimage sequencesdata compression |
spellingShingle | Raffaele Pizzolante Bruno Carpentieri Sergio De Agostino Adaptive Vector Quantization for Lossy Compression of Image Sequences Algorithms lossy compression adaptive vector quantization image sequences data compression |
title | Adaptive Vector Quantization for Lossy Compression of Image Sequences |
title_full | Adaptive Vector Quantization for Lossy Compression of Image Sequences |
title_fullStr | Adaptive Vector Quantization for Lossy Compression of Image Sequences |
title_full_unstemmed | Adaptive Vector Quantization for Lossy Compression of Image Sequences |
title_short | Adaptive Vector Quantization for Lossy Compression of Image Sequences |
title_sort | adaptive vector quantization for lossy compression of image sequences |
topic | lossy compression adaptive vector quantization image sequences data compression |
url | http://www.mdpi.com/1999-4893/10/2/51 |
work_keys_str_mv | AT raffaelepizzolante adaptivevectorquantizationforlossycompressionofimagesequences AT brunocarpentieri adaptivevectorquantizationforlossycompressionofimagesequences AT sergiodeagostino adaptivevectorquantizationforlossycompressionofimagesequences |