Next Article in Journal
Application of Gradient Descent Continuous Actor-Critic Algorithm for Bilateral Spot Electricity Market Modeling Considering Renewable Power Penetration
Next Article in Special Issue
Design and Implementation of a Multi-Modal Biometric System for Company Access Control
Previous Article in Journal
Searchable Data Vault: Encrypted Queries in Secure Distributed Cloud Storage
Previous Article in Special Issue
Towards Efficient Positional Inverted Index †
Open AccessArticle

Adaptive Vector Quantization for Lossy Compression of Image Sequences

1
Dipartimento di Informatica, Università di Salerno, Via Giovanni Paolo II, 132, Fisciano, SA 84084, Italy
2
Computer Science Department, Sapienza University, Via Salaria 113, Rome 00185, Italy
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in Data Compression Conference 2016, Communication Processing and Security 2016.
Academic Editors: Pierre Leone and Bruno Carpentieri
Algorithms 2017, 10(2), 51; https://doi.org/10.3390/a10020051
Received: 23 January 2017 / Revised: 24 April 2017 / Accepted: 4 May 2017 / Published: 9 May 2017
(This article belongs to the Special Issue Data Compression, Communication Processing and Security 2016)
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. View Full-Text
Keywords: lossy compression; adaptive vector quantization; image sequences; data compression lossy compression; adaptive vector quantization; image sequences; data compression
Show Figures

Figure 1

MDPI and ACS Style

Pizzolante, R.; Carpentieri, B.; De Agostino, S. Adaptive Vector Quantization for Lossy Compression of Image Sequences . Algorithms 2017, 10, 51.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Back to TopTop