Speeding-Up Image Encoding Times in the Spiht Algorithm

Abstract

In this paper, a new approach (scheme) to the analysis of quad-trees in the discrete wavelet spectrum of a digital image is proposed. During the pre-scanning phase, the proposed scheme generates problem-oriented binary codes for the whole set of quad-tree roots (wavelet coefficients) and thereby accumulates information on the significance of respective descendants (wavelet coefficients comprising quad-trees on the view). The developed scheme can be efficiently applied to any zero-tree based image coder, such as the embedded zero-tree wavelet (EZW) algorithm of Shapiro and set partitioning in hierarchical trees (SPIHT) by Said and Pearlman. Fairly impressive performance of the proposed quad-tree analysis scheme, in the sense of image encoding times, is demonstrated using the SPIHT algorithm and the discrete Le Gall wavelet transform.

A New Le Gall Wavelet-Based Approach to Progressive Encoding and Transmission of Image Blocks

“…To simplify description of the obtained results, we here introduce the following notations: the CPU time required to perform the (modified) integer Le Gall wavelet transform (DLGT) of the digital image (or that of ROI) is denoted by 1  , to perform wavelet- Three cases were analysed and compared: first of all, a non-compressed image was sent across the low communications channel to the user (Figure 3, a); secondly, lossless encoding (SPIHT algorithm with an improved quad-tree analysis scheme [9]) in the Le Gall wavelet domain was applied to the image, and compressed image was sent to the user (Figure 3, b); thirdly, the image was processed (lossy encoding by SPIHT), a "rough" estimate of the original image was sent to the user, the selected image blocks (ROI) were processed by applying both the (modified) integer DLGT and the SPIHT encoder, and then a high quality estimate of the selected ROI were sent again to the user's PC (Figure 3, c).…”

“…image; (b) additional bits are added to the ROI (4,6) X ( 8 T  , 1.38 KB, 0.02    ); (c) third quarter of the ROI (4,6) X , i.e. image block (9,13) X , in greater detail ( 2 T  , 3 KB, 0.08…”

“…In Figure 4 (b, c), some additional blocks (ROI) of the image Neck.bmp, namely, (4,6) X and (9,13) X , are processed and analysed. Compression details of the selected ROI are also indicated.…”

“…Some other interesting ideas and developments in the wavelet-based image compression field are presented in [9][10][11][12][13][14].…”

Investigation of Water Aeration Based on Digital Image Processing

“…6 is composed assuming that if a pixel value is > 145 it is taken as black and if < 145 it is taken as white. Image processing and analysis [36] was made using the Python code. Our code evaluates how the air concentration in the water is distributed in depth and in width.…”