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Title: Hybrid predictive, wavelet and arithmetic (HPWA) still image coding system
Author: Radi, Naeem M. M.
ISNI:       0000 0004 2733 8458
Awarding Body: Liverpool John Moores University
Current Institution: Liverpool John Moores University
Date of Award: 2012
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The vast spread of the use of mobile computing devices and social media prompted the need for higher compression ratios than those currently offered by JPEG and JPEG2000. Furthermore, the Increase of the processing power of computing devices made it possible to implement more sophisticated image coding algorithms or hybrid techniques that wouldn't have been possible in the past. A novel lossy image compression technique called Hybrid Predictive Wavelet and Arithmetic (HPWA) is proposed. This technique can achieve higher compression ratios in comparison to JPEG and JPEG2000 without noticeable loss in the image quality. A lossless image coding technique can be derived from the proposed lossy technique. HPWA uses Predictive Coding as a front end to the Discrete Wavelet Transform Coding. In this case, Transform Coding (Discrete Wavelet Transform) is enhanced by performing Predictive Coding first on the image and then passing the output to the next stage which is DWT. A very important point is to note that predictive coding will remove inter-pixel redundancy, while DWT will remove coding redundancy. The proposed H PWA consists of four stages. Stage 1 (Predictive Coding): The aim of using Predictive Coding in the HPWA system as a front-end to Discrete Wavelet Transform coding is different from the original aim of Predictive Image Coding as the actual image compression system; however the principle is still the same. The aim is to get the smallest mean square error (or the largest signal to noise ratio) of the prediction error data. A neural network based nonlinear predictor is used to predict the pixel values in the image since nonlinear predictors have proved to be more efficient in predictive image coding. Stage 2 (Quantisation): The quantiser is used usually to code the prediction error data in a compressed manner which results in reducing the size of the prediction errors file. A novel variable- length truly adaptive quantiser which outperforms the popular Lloyd-Max non-uniform quantiser was developed as part of this project. The improved results come at the expense of a relatively small amount of extra data that has to be saved or transmitted with the image. Stage 3 (Discrete Wavelet Transform): a standard DWT algorithm is used to transform the prediction error data (or optionally the quantised prediction error data) to frequency coefficients. It has been noted that up to 3 levels of decomposition (compression ratio of 64:1) gives very good compression rate without significant loss of accuracy when transforming the original image data using DWT. However, when the prediction error data was transformed; up to 5 or 6 levels of decomposition (compression ratio of 1024:1 or 4096:1) gives very good compression rate without significant loss of accuracy. Stage 4 (Arithmetic Coder): The fourth and final stage in the HPWA system is Arithmetic Coding (AC) which is a lossless technique. Arithmetic Coding is used to code the transform coefficients of the most significant frequency sub-band, the mean value of the coefficients in each of the neglected sub-band and the original image values of the two rows and three columns that are not coded in the first stage - Predictive Coding. The complete HPWA system was benched marked against JPEG2000 using 10 standard grey levels test images. JPEG2000 performed better at decomposition levels 1 and 2, i.e. for compression ratios 4:1 and 16:1. At decomposition level 3 (compression ratio 64:1), HPWA systems performed slightly better than JPEG2000. However, JPEG2000 completely failed at decomposition levels 4 (compression ratio 256:1) and beyond while the HPWA continued to perform well at decomposition levels 4 and 5 and even at level 6 for larger images. The average improvement offered by the proposed HPWA system over JPEG2000 in terms ofthe PSNR at decomposition level 4 and 5 is 5.05 dB and 13.75 dB, respectively.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available