A Fast and Accurate Prediction of Distortions in DCT-Based Lossy Image Compression

Author:

Abramova Victoriya12ORCID,Lukin Vladimir1ORCID,Abramov Sergey1ORCID,Kryvenko Sergii1ORCID,Lech Piotr3ORCID,Okarma Krzysztof3ORCID

Affiliation:

1. Department of Information and Communication Technologies, National Aerospace University, 61070 Kharkiv, Ukraine

2. Department of Optoelectronics, Center for Physical Sciences and Technology, 10257 Vilnius, Lithuania

3. Department of Signal Processing and Multimedia Engineering, West Pomeranian University of Technology in Szczecin, 70-313 Szczecin, Poland

Abstract

Since the number of acquired images and their size have the tendency to increase, their lossy compression is widely applied for their storage, transfer, and dissemination. Simultaneously with providing a relatively large compression ratio, lossy compression produces distortions that are inevitably introduced and have to be controlled. The properties of these distortions depend on several factors such as image properties, the coder used, and a parameter that controls compression, which is different for particular coders. Then, one has to set a parameter that controls compression individually for an image to be compressed to provide image quality appropriate for a given application, and it is often desirable to do this quickly. Iterative procedures are usually not fast enough, and therefore fast and accurate procedures for providing a desired quality are needed. In the paper, such a procedure for two coders based on discrete cosine transform is proposed. This procedure is based on a prediction of mean square errors for a given quantization step using a simple analysis of image complexity (local activity in blocks). The statistical and spatial–spectral characteristics of distortions introduced by DCT-based coders are analyzed, and it is shown that they depend on the quantization step and local content. Generalizing the data for sets of grayscale test images and quantization step values, it is shown that the MSE can be easily predicted. These predictions are accurate enough and can be used to set the quantization step properly, as verified by experiments performed using more than 300 remote sensing and conventional optical images. The proposed approach is applicable to the lossy compression of grayscale images and the component-wise compression of multichannel data.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

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