Method for realizing image compression based on folding flow line wavelet transformation

Abstract

The invention provides a method for realizing image compression based on folding flow line wavelet transformation, and particularly relates to a technology based on folding flow lines. The folding flow lines adopt a uniform architecture very large scale integration (VLSI) for the transformation of various wavelets, so that the resource consumption is reduced by structuring the shortest iterative calculation path for wavelet transformation, and the hardware utilization efficiency is improved by simplifying improving steps and adopting a single-shot input structure. An experiment result shows that for the same image, if the structure and a traditional long flow line structure are adopted for an experiment, the method is more excellent in resource consumption and hardware utilization rate.

Description

technical field [0001] The present invention relates to a VLSI (Very Large Scale Integration, Very Large Scale Integrated Circuit) implementation method of a unified architecture applicable to multiple wavelet transforms, in particular to a folding pipeline technology, which reduces computing resources by constructing the shortest iterative computing path of wavelet transform consumption, and by simplifying the upgrading steps and adopting an injective input structure to improve hardware utilization efficiency, it belongs to the technical field of digital image processing. Background technique [0002] Wavelet transform has a wide range of applications in the fields of signal analysis and digital image processing because of its unique time-frequency localization characteristics. In the 1990s, scholars such as Sweldens and Daubechies proposed a fast calculation format for wavelet signal decomposition and reconstruction - the lifting scheme. The basic idea of ​​the lifting sc...

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Problems solved by technology

Among them, the advantage of the 9 / 7 wavelet transform method is that the energy concentration of the wavelet transform and the PSNR (peak signal-to-noise ratio, the most commonly used objective evaluation index of image quality) of the restored image are relatively high, but its computational complexity is relatively high. VLSI It consumes more resources when implementing

The 5 / 3 biorthogonal wavelet transform has fewer calculation steps, low power consumption, and strong real-time performance due to its short wavelet base length and easy fixed-point transformation, but its energy concentration and PSNR of restored images are lower than those of the 9 / 7 wavelet transform poor performance

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