JPS6270980A - Compression system for plural pictures of same type - Google Patents

Abstract

PURPOSE:To obtain a compression rate higher than that obtained by a conventional system by making use of the correlation between adjacent picture elements within each of plural pictures of the same type as well as the correlation of corresponding picture elements between pictures. CONSTITUTION:The data inputted from a picture input device 11 is compressed by a processor 12 and the result of this compression is displayed on a display 13. At the same time, the compression data is stored in a magnetic disk 14 or a magnetic tape 15. For instance, a sheet of plural pictures of the same type is inputted as a reference picture f0 and the estimated value and an estimated error are calculated for the value of each picture element on the picture to obtain an estimated error picture e0. Then an objective picture f1 is inputted for calculation of an estimated error picture e1 carried out in the same way. In addition, the difference is secured between the corresponding picture elements of both pictures e0 and e1. Then the final estimated error picture g1=e1-e0 is calculated and the picture g1 is delivered in the form of Huffman codes.

Description

[Detailed description of the invention] [Field of application of the invention] The present invention relates to a compression method for multiple images of the same type.

[Background of the invention]

Among the conventional image data compression methods, a method with a high compression rate utilizes the high correlation between neighboring pixels on a digital image, and applies a certain prediction method to calculate the neighboring [ A method has already been proposed in which the prediction side error is obtained from the surface elements and Huffman coding is performed. however,
・It is predicted that images of the same head due to stains, for example, images obtained in mass examinations of chest There was a problem in that the standard compression process was not performed.

[Purpose of the invention]

The purpose of the present invention is to utilize both the correlation with adjacent pixels in images with the same fV number and the correlation between corresponding pixels between images, and achieve a higher compression rate than before. The purpose of this paper is to propose a compression method for multiple images of the same type.

[Summary of the invention]

In order to achieve the above object, the present invention analyzes not only the correlation within each individual image but also the correlation between each image for multiple images of the same type, for example, a large number of images obtained in mass examinations of helmets and chests. It is unique in that it is also used. Specifically, first, one image is selected from all the target images and used as a quasi-shield image. Next, as a first step, the conventional method is applied to the reference image and the target image.
A prediction error image is calculated for each. Furthermore, as a second step, a difference is taken between corresponding pixels of each prediction error image, and the image obtained here (one final prediction error image) is Huffman encoded. Fix the reference image and target surface 1
Huffman-encoded data for all images can be obtained by changing nu one after another.For unstructured reference images, the conventional method, which uses the high correlation between adjacent pixels in the image, is used. E-14 is performed by applying the human coding method.

[Invention no I7Iyaf+! I:] Hereinafter, a simple embodiment of the present invention will be explained with reference to FIGS. 1 and 2.

FIG. 1 shows a system diagram of an image compression apparatus to which the present invention is applied. Data input from the image input device 11 is compressed in the processor 12 according to the procedure described later, the compression result is displayed on the display 13, and at the same time, the compressed data is stored on the magnetic disk 14 or magnetic tape 15.

Hereinafter, the processing procedure in the processor 12 will be explained with reference to the flow chart shown in FIG. First, in step 21, one of the target multiple pictures of the same building, i'♂, is input as the reference image f6, and in step 22, one of the prediction methods proposed in the past is used to For each pixel value fkt, predicted value f, t, prediction error e kt
(= f yt - f bt ) 'e' J-out prediction error image e. get. Next, in step 23, the target image f+e is input, and in step 24, a prediction error image ei is similarly calculated. Furthermore, in step 25, the difference is taken between the corresponding pixels of e and e, resulting in a final prediction error image g1=e1-e.

Calculate. Then, in step 26, gI is Huffman encoded and output. Step 23 to Step 26
After repeating the steps up to the number of target images (N-1) times excluding the reference image, in step 27, eo is Huffman encoded and output, and the process ends. Note that it is possible to restore a plurality of original images from compressed data by completely reversing the series of processes described above.

〔Effect of the invention〕

According to the present invention, when compressing a plurality of images of the same type, the adjacent 11! in each image! Since not only the correlation between ii8 but also the correlation between each picture mound is used, a higher correction rate can be obtained compared to the conventional method.

[Brief explanation of drawings]

FIG. 1 is a system diagram of an image compression apparatus to which the present invention is applied, and FIG. 2 is a flowchart of compression processing procedures within a processor.

Claims (1)

[Claims] One of the multiple target images of the same type is used as a reference image, and for each pixel on the reference image and each target image other than this, the predicted value of the density value is determined from neighboring pixels, and the prediction error from the true value is calculated. 1. A method for compressing a plurality of homogeneous images, the method comprising the steps of: obtaining a prediction error image for each prediction error image; and calculating a difference between corresponding pixels of each prediction error image and Huffman encoding the difference value.