Image Compression Method

Following the development of digital age, the information which need to save, transport, transact is increasing exponential. As an important component of information, image which includes the most amount of information is the carrier of the information communicating. As one of the most components of Image Processing , Image Compression is always the research hotspot.The research background of this paper is the still image compression of the Second Generation ID card, and the aim of research is that saving RGB image in the 1024 bits. Under this condition, this paper put forward the optimizing truncation method and ROI coding method for the better compression performance. They both improve the SPIHT algorithm from the different direction and achieve state-of-the-art compression performance.SPIHT algorithm which Said and Pearlman put forward was based on Shapiro's EZW algorithm is one of the coding methods of Set Partitioning in Hierarchical Trees, and is one of the most efficient methods of still image compression coding. This algorithm which uses the Spatial Orientation Tree Structure and bit-plane coding does not only achieve the better compression coding efficiency, but also it outputs the embedded bit-stream and supports the different multi-rate decoder and makes for the progressive transmission of the image. Though the SPIHT algorithm can truncate the bit-stream at any point, this method can't be sure that the compression performance is the best quality at the current rate.So this paper put forward the optimizing truncation method for compensating this shortage. This method can be described that dividing the whole image into some blocks, and figuring the optimizing curves of the each block. When truncating the bit-stream, we can search the truncation point of every block on the optimizing curve of every block based on the optimizing curve of the whole image, so achieve state-of-the-art optimizing truncation to the whole image. The relation of rate and distortion can be described that the image have the one and only optimizing curve (Fig 1). Any algorithm is approaching this optimizing curve limitlessly. The optimizing curve can show the minimum distortion performance at the different constrained rate. Fig 1 The optimizing curveThe optimizing truncation method is put forward based on the fact of that the image has the one and only optimizing curve. At the constrained rate, the method's mathematics model is as follows: (1)while the image is divided into blocks,, Here, denotes the distortion of the whole image; denotes the bits of the whole image because the rate and the bits of the image is direct ratio; and denote respectively the distortion and bits of the th block.It does well know that this equation is in fact the Lagrangian function. And the constrained condition is that the sum of all the block's bits subject to a constraint (), the equation is as follows: (2)More generally, the optimizing truncation method is to find the point whose value of x-axis denotes the current rate and value of y-axis denotes the distortion of the image on the optimizing curve, figure the tangent line on this point and computer the slope value of the tangent line under the constraint condition. We can search the corresponding truncating point of each block by the slope value of those points being equal to and achieve the optimizing truncation to the image.The optimizing truncation method can be performed as follows:Wavelet transforming to the image;Dividing the image to some blocks, the principle of dividing is described that we define the maximum size of the block at the highest resolution level, define the quarter of the maximum size at the next resolution level, and so on;The distortion equation is as follows: (3)Here, denotes the ith block's distortion with truncation point of , denotes...