JPH03181231A - Image processing method - Google Patents

Abstract

PURPOSE:To improve the picture quality by multiplying a discrete cosine conversion coefficient by a high band emphasizing coefficient before the quantization processing is carried out at the compression of data. CONSTITUTION:The image data is compressed via the processing for the discrete cosine conversion, the quantization, etc. In this case, a conversion coefficient Cij (i, j=1, 2...8) is obtained via the discrete cosine conversion by a high band emphasizing coefficient Aij for the emphasis of a high band component. Then the quantization processing is carried out. In this processing system, the discrete cosine conversion coefficient Cij is outputted as the frequency component of an image signal. Thus the high band can be emphasized when the coefficient Cij is multiplied by the coefficient Aij. Then it is not required to use such a complicated filter constitution as the conventional one, and the addition of only one multiplier suffices. Consequently, a reproduced image having its emphasized contour part is obtained.

Description

DETAILED DESCRIPTION OF THE INVENTION [Field of Industrial Application] The present invention relates to an image processing method for compressing still images and transmitting or recording the data.

[Conventional technology]

In order to standardize the natural image encoding method, “Ba5eli
ne System”, Extended, Syst
Various internationalization standard systems such as "EM" have been proposed.

Figure 3 shows “BaselineS”, one of the internationalization standard systems.
This system divides one input image into blocks of 8 x 8 pixels, and performs discrete cosine transformation (DCT) on each block.
: Discrete Co51neTransfo
rm) (processing P1), and the obtained DCT coefficients are
Quantization is performed by dividing by each threshold value of a quantization matrix consisting of eight threshold values (process P2).

The difference between the DC component of the quantized DCT coefficient and the DC component quantized in the previous block is taken, and the number of bits of the difference is Huffman encoded. After being zigzag scanned in the ACC bone block and converted into a one-dimensional sequence,
Two-dimensional Huffman encoding is performed using the number of consecutive zeros (invalid coefficients) and the number of bits of valid coefficients (processes P3 and P4).

4 and 5 are diagrams showing a quantization matrix for luminance signals and a quantization matrix for color difference signals, and FIG. 6 is a table showing the order of zigzag scan.

Note that during quantization in process P2, quantization is performed after each threshold value of the quantization matrix is multiplied by a certain coefficient (scale factor).

This scale factor adjusts the image quality and compression rate of the compressed image.

The data compressed in this way is decompressed by a process opposite to processes P1 to P4. That is, Huffman decoding in process P4', decoding of DC and AC components in process P3', inverse quantization in process P2', and inverse DCT (IDCT) in process PL'.

[Problem to be solved by the invention]

In the above-mentioned processing method, data compression is performed at the expense of image quality by performing coarse quantization on high-frequency components after DCT transformation, resulting in unclear contours of the image quality. There is an inconvenience.

Conventionally, a method using two-dimensional filter processing has been adopted for contour emphasis (Journal of the Television Society Vol. 1.3).
7. No. 10 'Fully digital electronic still camera'
), this method requires a large number of multipliers and adders when implemented in hardware, which is undesirable as it complicates the circuit configuration.

An object of the present invention is to provide an image processing method that can improve image quality during compression and emphasize outlines using a simple method.

[Means to solve the problem]

In this invention, - digital images are divided into 1 block n×
Divide into multiple blocks each consisting of n pixels, perform discrete cosine transformation for each block, and obtain n×
Quantization is performed by multiplying n transform coefficients by n x n high frequency emphasis coefficients, and dividing the value obtained as a result of this multiplication by each threshold of a quantization matrix consisting of n x n thresholds. Thus, data compression and emphasis processing of the digital image are performed simultaneously.

[Function] The image processing method according to the present invention, when compressing image data by processing such as discrete cosine transform or quantization, multiplies the transform coefficient obtained by the discrete cosine transform by a high-frequency emphasis coefficient to achieve a high yi. After emphasizing the precepts, quantization processing is performed.

According to this processing method, the discrete cosine transform coefficients are output as frequency components of the image signal, so high-frequency emphasis can be achieved by multiplying the transform coefficients by the high-frequency emphasis coefficient. There is no need for a complicated filter configuration, and only one multiplier is required.

In this way, it is possible to obtain a reproduced image with enhanced image quality and enhanced contours with a simple circuit configuration.

〔Example〕

FIG. 1 is a schematic diagram showing an embodiment of the processing procedure of the image processing method according to the present invention, and the same parts as in FIG. 3 are given the same reference numerals and will be explained.

First, the input image is divided into blocks consisting of n dots in the horizontal direction and n lines in the vertical direction of n×n pixels (for example, 8×8 pixels), and each block is subjected to discrete cosine transformation (DCT).
) is done (processing PL).

Then, the DCT coefficient Caj obtained by this DCT
(1+j=1+2+ . . . , 8) is multiplied by a high frequency enhancement coefficient A i j consisting of 8×8 coefficients of “1” or more to perform high frequency enhancement processing (processing P5). The calculation process here is the multiplication of each element of the conversion coefficient Ci j and the emphasis coefficients A and j (CC4J' = C1jXAij).

FIG. 2 shows an example of high-frequency emphasis coefficients Aij.

For high frequency enhancement, filter processing is often used to emphasize the high frequency components of the signal, but since the processing procedure shown in Figure 1 includes DCT processing, the frequency components of the image signal are converted to DCT. Since it is output as a coefficient, high-frequency emphasis can be realized by multiplying the transform coefficient by the coefficient A ij to increase the transform coefficient.

The transform coefficient Ci j' thus high-frequency emphasized is then divided by each threshold value of a quantization matrix consisting of 8×8 threshold values to perform quantization (processing P2).

The difference between the DC component of the quantized DCT coefficient and the DC component quantized in the previous block is taken, and the number of bits of the difference is Huffman encoded. AC$.

A zigzag scan is performed within the block to convert it into a one-dimensional number sequence, and then two-dimensional Huffman encoding is performed using the number of consecutive zeros (invalid coefficients) and the number of bits of effective coefficients (Processes P3 and P4 ).

Huffman encoding does not use the quantized coefficient values themselves for the DC component and ACC component, but the number of bits necessary to express the values is the object of Huffman encoding. In addition to the Huffman code, the value of the number of bits is added as additional information.

For example, if the quantized coefficient is 2 (decimal number), it will be expressed in binary number as 000...010'', but
The number of bits 2 required to express this is Huffman encoded as a value representative of this value, and 2-bit data "10" is added as an additional bit.

On the other hand, if the quantized coefficient is negative, 1 is added from the additional bit.
The data after subtracting is added. For example, if the quantized coefficient is -2 (decimal number), when expressed in binary number (two's complement representation) it becomes "111...110", and the lower two bits are additional bits, but ° "10 "01", which is obtained by subtracting "l" from ", is added as an additional bit. By doing this, when the quantized coefficient is positive, the additional bit starts with 1, and when it is negative, it starts with O, making it easy to determine whether it is positive or negative.

[Effect of the invention] According to this invention, before quantization processing during data compression,
Since the DCT coefficient is multiplied by the high frequency emphasis coefficient,
Compared to the conventional method, the image quality is improved by emphasizing the contours of the reproduced image, and the circuit configuration is simplified because only one multiplier needs to be added for hardware implementation.

[Brief explanation of drawings]

FIG. 1 is a diagram showing the processing procedure of the image processing method according to the present invention, FIG. 2 is a diagram showing the matrix of high frequency enhancement coefficients, FIG. 3 is a diagram showing the processing procedure of the conventional image processing method, and FIG. 4 is a diagram showing the processing procedure of the conventional image processing method. 5 is a diagram showing a quantization matrix of a luminance signal, FIG. 5 is a diagram showing a quantization matrix of a color difference signal, and FIG. 6 is a diagram showing a zigzag scan table.

Claims (1)

[Claims] Divide one digital image into multiple blocks each block consisting of n×n pixels, perform discrete cosine transform for each block, and convert the resulting n×n transform coefficients into n×n The data compression and An image processing method characterized by simultaneous emphasis processing.