1. Field of the Invention
The present invention relates to a tonal image data compression apparatus suitably applied for compressing a medical image such as an X-ray image and, more particularly, to an image data compression apparatus capable of achieving a high compression ratio without any deterioration of the image quality and achieving a reconstructed image of a high quality without any sacrifice of the compressibility of the image data.
2. Description of the Prior Art
A medical image such as the X-ray image is required to have a high digital conversion accuracy because it is used for the diagnoses and treatments by the doctor.
Thus, if the image data of the medical image are digitized, the amount of information digitally converted is massive in case. If, for example, a plain X-ray image (having tonal image data) of a chest taken on a large size film is digitized by means of a laser scanner, the amount (i.e., the storage capacity) of information is as much as 4 to 5M bytes for each image.
Considering the cost and transmission time for saving image data and transmitting them through communication lines, it is expedient to compress the image data before storage.
The technology for compressing the image data divided into reversible and irreversible compression technologies.
The former is a compression technique having a compression ratio of about 2:1 to 3:1, in which a reproduced image obtained by compressing/decompressing an original image is completely identical to the original image.
In the latter irreversible compression technique, on the contrary, the compression ratio obtainable is more than 5:1 although the reproduced image contains more errors. Therefore, recent investigations have been directed to improving the image quality of the irreversible compression technique.
The irreversible compression technique is exemplified by techniques using the orthogonal transforms. Of these, the cosine transform coding technique compresses the image data by using the transform called the cosine transform.
This cosine transform is one of the orthogonal transforms which are represented by the Fourier transform or the Hadamard transform. It is known that the best compression ratio can be attained if the orthogonal transformation is used.
In cosine transform coding, the properties of the image data are transformed into a compressible form by the cosine transform. Image data having various density distributions can be transformed into the fixed image properties having a small image dependency.
The fixed image data properties can be given by a Gaussian distribution having its peak at zero.
For example, one image is divided into a plurality of blocks, and the frequency of occurrence of the image data, which are obtained by digitizing the block image of one block, against each picture element level (or density level) is different, as shown in FIGS. 21A or 22A, in dependence upon the image characteristics of the block image.
If, however, those frequencies of occurrence are subjected to the cosine transform, the block image of FIGS. 21A or 22A is caused to take the coefficient distribution (the Gaussian distribution), as shown in FIGS. 21B or 22B, i.e., a form having no dependency upon the image characteristics.
Here, the abscissa of the Gaussian distribution designates the transform coefficient of the AC component obtained by the cosine transform, and the ordinate designates the frequency of occurrence.
Since the transform coefficient has a real number value, the dynamic range of the coefficient is reduced if the transform coefficient is quantized. The image data can be compressed by coding the transform coefficient thus quantized.
FIG. 23 is a block diagram showing an essential portion of a conventional example of an image data compression device using the cosine transform coding described above.
The image data (having 10 bits per pixel in the present example), such as the aforementioned medical image to be compressed, are stored in a frame memory 2. In this example, the block size N is sixteen picture elements in both the line and column directions.
The block image data (or density data) are fed to a two-dimensional discrete cosine transformer (2D-DCT) 20 functioning as the orthogonal transform. The image data f (i, j) (i and j=0, 1, - - - , and 15) are subjected to the cosine transform to give 256 transform coefficients for one block. These transform coefficients are one direct current component (i.e., DC component) and 255 alternating current components (i.e., AC components).
Next, the 255 transform coefficients (i.e., the AC components) thus obtained from the 256 transform coefficients are fed to a quantizer 50 so that they are quantized with a predetermined quantization width outputted from a quantization width controller 3, until they are coded by a coding device 60. By the combination of quantization and coding, the image data compressed. Huffman Codes, Run Length Codes and Arithmetic Codes, etc. can be used for coding purposes.
The coded data are sent from a terminal 70 or stored in a memory (not shown).
In this transform coding, a high compression ratio is obtained by rounding the transform coefficients.
FIG. 6 is a chart showing the behavior of quantization. W and 2S indicate "quantization width". And .+-.a.sub.n (n=.phi., 1, 2, . . . ) means quantized transform coefficients.
In this case, AC components with value greater than -S and less than +S are rounded off to zero (=a.sub.0). At this time, one half of the quantization width 2S is called cutback threshold value S. The quantization for reducing the coefficients to zero by using the cutback threshold value S is well known as the "threshold coding", which can in the wide sense be thought as one of the quantizations.
Thus, the transform coefficients obtained by orthogonally transforming the image data, as described above, exhibit a tendency to concentrate most of the information for constructing the image at the coefficients of a lower frequency.
In this transform coding, therefore, the transform coefficients of higher frequency are usually cut off in the so-called "high-frequency cuttoff" before the coding is accomplished to compress the image data with a high compression ratio.
This method of cutting off the transform coefficients existing in the high-frequency side is well known as the "zonal coding".
However, the transform coding using the aforementioned zonal coding is accompanied by the following disadvantages. Since the block containing an image having intense density changes at the edges has some AC components of large amplitude at the higher-frequency range, the edges of the reconstructed image are blurred which deteriorates the image quality and the SN ratio seriously if the transform coefficients at the higher-frequency range are drastically cut off.
These image quality deteriorations cause the block boundary artifacts (in which the block boundary becomes prominent) to raise a fatal defect especially in a medical image for diagnosis.