JPH0352386A - Picture data compression method - Google Patents

Abstract

PURPOSE:To improve the effect of data compression as a whole by converting a difference according to a predetermined nonlinear quantization characteristic when an absolute of the difference between a predicted value and actual gradation does not exceeds prescribed value and applying 2-dimension Huffman coding based on both the converted difference and the run length. CONSTITUTION:The method consists of a picture input section 1, a prediction value calculation section 2, a prediction error calculation section 3, a quantization section 4, a Huffman coding table 5 and a coding section 6. When the absolute value of a difference between a prediction value and actual gradation does not exceed a prescribed value and the difference is converted according to a predetermined nonlinear quantization characteristic, the 2-dimension Huffman coding table is generated based on number where the converted difference and picture elements with equal converted difference appear consecutively, and the code is assigned based on the coding table. Thus, an optimum 2-dimension Huffman coding table is generated in response to the frequency of occurrence of events and the code is assigned to obtain a high data compression effect.

Description

[Detailed Description of the Invention] [Field of Industrial Application] The present invention relates to a method for compressing and encoding multi-tone image data,
In particular, it relates to improvements in predictive coding methods.

[Conventional technology]

Predictive coding is widely used as a highly efficient coding method for multi-gradation image data (Takahiko Fukinuki, "Digital Signal Processing of Images" (expanded edition), pp. 146-16)
4, Nikkan Kogyo Shimbun, published August 25, 1987). Generally, in predictive coding methods, the gray level value of the current pixel is predicted using a prediction function based on the gray level values of the surrounding encoded pixels. Furthermore, the difference value between this predicted value and the actual gradation value is transformed according to predetermined nonlinear characteristics (nonlinear quantization), and a code corresponding to the transformed difference value is assigned.

However, when using this method to compress and encode image data input from an image input device, there are the following problems.

The nonlinear characteristics applied to the nonlinear quantization of the difference values are mainly based on the frequency of appearance of the difference values.

In other words, for parts with high frequency of appearance, the difference value is divided into cases in fine increments, and conversely, parts with low appearance frequency are divided into cases in rough increments, and the representative value corresponding to each case is converted into a converted difference value. That is to say. In general, the frequency of appearance of difference values tends to be higher as the absolute value is smaller, and lower as the absolute value is larger. Therefore, regarding the relationship between the difference value and the converted difference value, as the absolute value of the difference value becomes larger, the range of the difference value corresponding to one converted difference value becomes larger. This shows that the larger the absolute value of the difference value, the greater the possibility that the error between the actual difference value and the converted difference value will become larger. This error results in an error between the tone values of the original image and the tone values of the restored image when the encoded image data is decoded. Generally, the difference value becomes large in a portion where the number of gradations of the original image changes drastically, such as an edge portion. Therefore, if the absolute value of the difference value is converted to a smaller value than the actual value as described above, the change in the number of gradations in the restored image will be more gradual than the change in the number of gradations in the original image, for example, This means that some parts will be blurred. Furthermore, in predictive coding, new pixels are predicted based on the gradation values of pixels that have already been encoded, so once a large error occurs, it will adversely affect subsequent pixels.
However, in image copying, the edge area contains extremely important information, and blurring this area will significantly degrade the image quality. Conventionally, as a method to improve this, for example, Japanese Patent Application Laid-open No. 56
As shown in Publication No. 129482, it is known that when the absolute value of the difference value exceeds a certain value, a tone value is encoded instead of the difference value to prevent error propagation. ．． However, in this method, if the difference value is less than a predetermined value, the difference value is encoded as is without being converted. If the number of gradations in the original image is about 16, the compression effect will be improved even with this method, but if it becomes about 256 gradations (8 bits), all the difference values whose absolute value is less than a predetermined value will be encoded as is. does not have much compression effect. [Problem to be solved by the invention]

Claims (1)

[Claims] (1) A method of compressing and encoding multi-gradation data, in which the gradation value of a pixel of interest is predicted using a prediction function based on the gradation values of encoded pixels around it, and the predicted value and The difference value from the actual gradation value is calculated, and if the absolute value of the difference value exceeds a predetermined value, the gradation value is converted according to a predetermined linear quantization characteristic, and the converted gradation value is In an encoding method, a corresponding code is assigned, and when the absolute value of the difference value does not exceed a predetermined value, the difference value is transformed according to a predetermined nonlinear quantization characteristic, and a code corresponding to the transformed difference value is assigned. , when the absolute value of the difference value does not exceed a predetermined value and the difference value is converted according to a predetermined nonlinear quantization characteristic,
An image characterized in that a two-dimensional Huffman encoding table is generated based on the transformation difference value and the number of consecutive pixels with the same transformation difference value, and a code is assigned based on the encoding table. Data compression method.