Abstract

Problem statement: Low complexity image compression algorithms are necessary for modern portable devices such as mobile phones, wireless sensor networks and high constraint power consumption devices. In such applications low bit rate along with an acceptable image quality are an essential requirements. Approach: This study proposes low and moderate complexity algorithms for colour image compression. Two algorithms will be presented; the first one is intensity based adaptive quantization coding, while the second is a combination of discrete wavelet transforms and the intensity based adaptive quantization coding algorithm. Adaptive quantization coding produces a good Peak Signal to Noise Ratio (PSNR), but with high bit rates compared with other low complex algorithms. The presented algorithms produce low bit rate whilst preserving the PSNR and image quality at an acceptable range. Results: Experiments were performed using different kinds of standard colour images, a multi level quantizer, different thresholds, different block sizes and different wavelet filters. Both algorithms considered the intensity variation of each colour plane. At high compression ratios the proposed algorithms produced 1-3 bpp bit rate reduction against the stand alone adaptive quantization coding for the same image quality. This reduction was achieved due to dropping of some blocks that claimed to be low intensity variation according to a comparison with predefined thresholds for each colour plane. The results show that the bit rate can be reduced by 72-88% for each low variation image block from the original bit rate. Conclusion: The results obtained show a good reduction in bit rate with the same PSNR, or a slightly less than PSNR of a standalone adaptive quantization coding algorithm. Further bit rate reduction has been achieved by decomposing the input image using different wavelet filters and intensity based adaptive quantization coding. The proposed algorithm comprises a number of parameters to control the performance of the compressed images.

Highlights

  • Image compression can be classified into lossy and lossless

  • Many algorithms have been developed by researchers for both image compression types; some of these algorithms use Discrete Cosine Transform (DCT) or Discrete Wavelet Transform (DWT), while others avoid the complexity of applying such transformation

  • Where, [:] represents ceil function, while the number of bits required for the bit plane of high variation blocks was computed accoriding to Eq 6: Intensity Based Adaptive Quantization Coding (IBAQC): This algorithm is a composition of AQC

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Summary

INTRODUCTION

Image compression can be classified into lossy and lossless. The lossy type aims to reduce the bits required for storing or transmitting an image without considering the image resolution much. Many algorithms have been developed by researchers for both image compression types; some of these algorithms use Discrete Cosine Transform (DCT) or Discrete Wavelet Transform (DWT), while others avoid the complexity of applying such transformation. Different approaches have been proposed for image compression using DWT, such as the adaptive algorithm for scalable wavelet image coding (Yuk-Fan et al, 2008). This algorithm was based on the regularity and features of images. & Applied Sci., 4 (4): 504-512, 2011 was implemented by (Delp and Mitchell, 1979) This second computes the intensity based AQC algorithm algorithm is used for grey-scale image compression parameters of the one level decomposed image. Some of these algorithms use the same two level quantizer, while others use a three-level quantizer

MATERIALS AND METHODS
RESULTS AND DISCUSSION
CONCLUSION
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