Deep Concatenated Residual Networks for Improving Quality of Halftoning-Based BTC Decoded Image.

Heri Prasetyo,Chih-Hsien Hsia,Alim Wicaksono Hari Prayuda,Jing-Ming Guo

doi:10.3390/jimaging7020013

Abstract

This paper presents a simple technique for improving the quality of the halftoning-based block truncation coding (H-BTC) decoded image. The H-BTC is an image compression technique inspired from typical block truncation coding (BTC). The H-BTC yields a better decoded image compared to that of the classical BTC scheme under human visual observation. However, the impulsive noise commonly appears on the H-BTC decoded image. It induces an unpleasant feeling while one observes this decoded image. Thus, the proposed method presented in this paper aims to suppress the occurring impulsive noise by exploiting a deep learning approach. This process can be regarded as an ill-posed inverse imaging problem, in which the solution candidates of a given problem can be extremely huge and undetermined. The proposed method utilizes the convolutional neural networks (CNN) and residual learning frameworks to solve the aforementioned problem. These frameworks effectively reduce the impulsive noise occurrence, and at the same time, it improves the quality of H-BTC decoded images. The experimental results show the effectiveness of the proposed method in terms of subjective and objective measurements.

Highlights

The block truncation coding (BTC) is a type of lossy image compression technique under the block-wise processing manner [1]
We firstly describe the image sources including the process of making the halftoning-based block truncation coding (H-BTC) image dataset
A deep learning approach for H-BTC image reconstruction has been presented in this paper

Summary

Introduction

The block truncation coding (BTC) is a type of lossy image compression technique under the block-wise processing manner [1]. Each image block is processed individually to yield two extreme quantizers, namely high and low mean values, and a binary image. The high and low mean values are computed in such a way using the average value (mean value) and standard deviation on each processed image block. The magnitude of high mean value is higher compared to the low mean value These two means keep the image block statistics unchanged. A pixel value of a binary image is replaced with high or low mean value. From this point of view, the BTC compression is very easy to implement. The false contour and blocking artifacts often destroy the quality of the BTC decoded image. These artifacts are more noticeable while the size of the image block is increased

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Methods

Results

Conclusion