Image compression and decompression using predictive coding and error diffusion

Abstract: An encoding/compression technique using a combination of predictive coding and run length encoding allows for efficient compression of images produced by error diffusion. The compression process comprises predictively coding (S50-S200) an error diffused image made up of a plurality of error diffused signals into a predicted image; comparing said error diffused image with said predicted image; generating (S300) a plurality of prediction errors from said comparison; and compressing (S700; Fig. 2B) said plurality of prediction errors using run length encoding. A decompression technique, and apparatus for carrying out the compression or decompression, is also disclosed.

Description: This invention relates to a lossless compression technique and an apparatus in which an image produced by error diffusion is predictively run length encoded and decoded. Data compression systems have been used to reduce costs associated with storing and communicating image data. However, conventional compression methods yield very little compression when the input image is halftoned by error diffusion. Error diffusion is an important technique for digital halftoning. It usually generates images with superior quality. Due to irregular, high frequency noise patterns introduced in the process, it is very difficult to compress error diffused images. For example, conventional run length based techniques, such as CCITT (ITU) Group3 and Group4 formats used in facsimile systems, are inappropriate for compressing error diffused images since they cannot suitable handle short run lengths which are dominant in error diffused images. U.S. Patent No. US-A-5,282,256 describes a method for encoding and decoding continuous image data, by converting an eight bit image signal to a one bit image signal and diffusing the conversion error throughout the one bit image signal. A predicted value, that is based on previous values of the one bit image signal, is used to replace selected pixels of the error diffused one bit image signa. The predicted value is incorporated into the error diffusion method. The amount of error diffused one bit image signal data is then reduced by "thinning out" or removing the predicted value from the one bit image signal data stream. The input image may be accurately reproduced because the decoding method uses the same prediction table that is used to encode the image data. However, US-A-5282256 does not increase the run-length of the image data, rather, the amount of image data is slightly reduced. Therefore, the encoded image data signal cannot be efficiently compressed because of the short run length. It is therefore an object of this invention to compress an error diffused image without losing image information. It is another object of this invention to use a predictive coding technique for the lossless compression of error diffused images. It is still another object of this invention to predictively code an error diffused image using run length encoding to realize lossless image compression. It is still a further object of this invention to predictively code and decode an original error diffused image using run length encoding/decoding. To achieve these and other objects, the inventive method and apparatus predictively code error diffused images to run length encoded error signals which can be transmitted or otherwise sent to a complementary receiver/decoder. According to a first aspect of the present invention, we provide a method for lossless efficient un-length encoding of error diffused halftone images as defined in claim 1, According to a second aspect of the present invention, we provide a method for lossless decompression of a run-length encoded signal as defined in claim 5, According to a third aspect of the invention, we provide an apparatus for lossless compression of an error diffused halftone digital signal as defined in claim 8. These and other aspects of the invention will become apparent from the following descriptions which illustrate a preferred embodiment of the invention when read in conjunction with the accompanying drawings in which: Figure 1 shows a block diagram of an illustrative apparatus using the compression method of an embodiment of the invention; Figures 2A and 2B are flow charts relating to an illustrative compression method of an embodiment of the invention; Figure 3 shows a block diagram of an illustrative apparatus using the decompression method of an embodiment of the invention; and Figures 4A, 4B and 4C are flow charts relating to an illustrative decompression method of an embodiment of the present invention. Unless otherwise indicated, the term "signal" is used interchangeably herein to mean both an individual signal and a multi-component signal having numerous individual signals. Referring now to the drawings, a basic system for carrying out the compression method in one embodiment of the present invention is shown schematically in Fig. 1. As shown, an image produced by error diffusion is received as the input of a compressor 2 at point 1. The image comprises a plurality of signals b(m,n) (hereafter "b"), which is typically, 1 or 2 bits per pixel. The compressor 2 comprises prediction circuitry 3 such as a predictor, comparison circuitry or subtraction circuitry 4 and a run length encoding means or encoder 5. The compressor 2 also includes as a part of the comparison circuitry 4, circuitry for generating a plurality of prediction errors 6 based on the results derived by the comparison circuitry 4. The signal b is input into the predictor 3 and also into the comparison circuitry 4. The predictor 3 generates a predicted signal, b* (m,n) (hereafter "b*") based on stored values of previous quantization errors, e*(m-i, n-j), and an average of previous signals b(m-i, n-j). The signal b, and predicted signal b*, are input into the comparison circuitry 4. This circuitry generates a plurality of prediction errors, E(m,n) (hereinafter "E"), by comparing the predicted image to the error diffused image. Prediction errors are determined by subtracting the predicted image from the error diffused image. Prediction errors are output at point 7 and input into a run length encoder 5. In this manner, only the differences or errors between an error diffused signal or image and a predicted image is transmitted to point 8. Figures 2A and B are flow charts showing the operation of the compressor 2 shown in Fig. 1. Initially, an error diffused signal, b, is input into the compressor 2 at step 550 (Fig. 2A). At step S100 a predicted, modified signal i* mod (m,n) (hereinafter "i* mod "), is calculated in the predictor 3 according to the following equation: (1) i* mod (m,n) = i*(m,n) + ∑a(i,j)e*(m-i,n-j) where the * denotes an estimation or prediction and a(i,j) denotes weights used in error diffusion. The two values on the right side of equation (1) represent an average for previous error diffused signals for a set of past pixels and a weighted summation of previous quantization errors, respectively. Both values are stored in a memory or storage (not shown in Fig. 1) which may be a part of, or separate from, the compressor 2. Once the predicted, modified signal i* mod is determined, the next step is to predict b* from a quantized value of i* mod at step 5200. Mathematically the predicted signal b* is calculated as follows: (2) b*(m,n) = Q[i* mod (m,n)] where Q is the quantization operation. In this manner the error diffused signal is predicted as b*. Once b* is known, the prediction error E can be calculated using the equation: (3) E(m,n) = b(m,n)- b*(m,n). The prediction error is calculated in the comparison circuitry 4 by subtracting the predicted image from the error diffused image. Such a comparison results in a plurality of prediction errors being generated by comparison circuitry 4 at step 5300. If during such comparisons a prediction error E is determined (step 5400) to be zero, then the predictor 3 has predicted a correct signal. In such a case there is no need to adjust i* mod . On the other hand, if during such comparisons (step 5500; Fig. 2B) E ≠ 0, then i* mod must be adjusted in step 510 (see Fig. 2B) in one of two ways. If: (4a) i* mod = minimum value in a quantization interval of b(m,n), if E(m,n) > 0; or (4b) i* mod = maximum value in a quantization interval of b(m,n), if E(m,n) < 0. For example, if 0.5 is used as a threshold for quantization then a "minimum" value corresponding to equation 4(a) would be a value greater than 0.5 e.g., 0.51 (b = 1 in this case and the quantization internal is any value greater than 0.5). Likewise, a "maximum" value would be a value less than 0.5, e.g., .49 (b = 0 in this case, and the quantization interval is any value less than 0.5). The actual adjustment is carried out by adjustment circuitry which may be apart of the predictor 3. In either equation 4(a) or (b) a new, predicted quantization error, e*(m,n) (hereafter "e*") must eventually be calculated and stored in the predictor 3 at step 5600. This quantization error is calculated using the following formula: (5) e*(m,n) = i* mod (m,n)-b(m,n). As can be seen from equation (5) a present predicted quantization error, e*, is derived from a predicted, modified signal, i* mod , and b. This present quantization error is then stored in memory in order to calculate a new, predicted modified signal when the next error diffused signal is input into the predictor 3. After the prediction errors are generated they are output to point 7 and eventually input into a run length encoder 5, for run length encoding of "E" (at step 5700). This is performed until all "E"s have been processed. Typically, error diffused signals have short "run lengths. " A run length is defined as a group of continuously coded pixels, i.e. 10 white pixels represented by binary 1s in a row. The use of predictor 3 increases the run length of error diffused signals. The use of a run length encoder 5 allows these "lengthened" run lengths to be transmitted or otherwise output using a code which identifies, in this instance 10 "non-errors" (as opposed to 10 white pixels) in a row by, for instance "10x" (where x = error) instead of transmitting each of the 10 "non-errors" individually, i.e., 1x, 1x, 1x...etc. Thus, it can be seen that in the event there are no errors generated, a continuous stream of "no errors" is input into the run length encoder 5. This continuous stream can be encoded by the run length encoder 5 and output to point 8 as one code indicating "no errors." Figure 3 depicts a schematic block diagram of an apparatus according to one embodiment of the invention which receives, decodes and decompresses the run length encoded image transmitted or otherwise sent from the apparatus shown in Fig. 1. As shown in Fig. 3, a decompressor 9 comprises run length decoding means or decoder 11, predictive coding circuitry or receiver predictor 12 and addition means or circuitry 13. The run length decoder 11 decodes run length encoded prediction errors input from point 10 and outputs a plurality of prediction errors at point 14. Prediction circuitry 12 inputs at point 15 past decoded signals output from addition circuitry 13 and outputs a plurality of predicted signals at point 16. These predicted signals are calculated within the prediction circuitry 12 by quantizing a predicted modified signal, i* mod . This signal, i.e., i* mod , is in turn first calculated by circuitry, preferably a part of the prediction circuitry, from an average of past decoded signals over a "neighborhood" and previous receiver quantization errors which are stored in receiver memory or storage (not shown in Fig. 3) in the same manner as in equations (1) and (2). The receiver memory or storage may be a part of, or separate from, the decompressor 9. The predicted signals from point 16 and prediction errors from point 14 are thereafter input into the addition circuitry 13 which adds the two signals together. In this manner, each predicted signal from the prediction circuitry 12 is added to each decoded prediction error from the run length decoder 11. As in the compressor 2, if at any point no prediction error is present, then the predicted signal is output as the decoded signal from the addition circuitry 13 to form an image at point 17. If an error exists, the corresponding predicted, modified signal is adjusted in the same manner as in equation (4). A decoded image can be output to a printer 18 or other reproducing apparatus. Each decoded signal is generated from each addition of a predicted signal and a decoded prediction error. In order to decode and correctly predict the next image or signal a present receiver quantization error is calculated within the decompressor 9. This error is then stored in the receiver memory. Similarly, Figs 4A, 4B and 4C are flow charts depicting the decompressing operation of the illustrative apparatus shown in Fig. 3. The operative steps of the decompressor (51050 to S1600) are analogous to the operation of the compressor with the exception that in step 51200 a decoded output b'(m , n) is first calculated from a decoded error as follows: b'(m,n) = E(m,n) + b* (m,n).

Claims: A method for lossless efficient run-length encoding of error diffused halftone images, comprising: inputting an error diffused halftone digital signal (b) (step 50); generating a predicted continuous tone signal (i* mod) from previous error diffused halftone digital signals (step 100); generating a predicted error diffused halftone digital signal (b) from the predicted continuous tone signal; generating a prediction error signal from the error diffused halftone digital signal (b) and the predicted error diffused halftone digital signal (b*) (step 300); and run length encoding the prediction error signal (E) (step 700). The method of claim 1, wherein the step of generating a predicted continuous tone signal comprises averaging a plurality of portions of previous error diffused halftone digital signals (step 100). The method of claim 2, wherein the step of generating a predicted continuous tone signal comprises; averaging previous error diffused signals; determining a weighted sum of previous prediction error signals; and summing the average of previous error diffused signals and the weighted sum of previous prediction error signals. The method of claim 3, further comprising replacing the predicted continuous tone signal with a minimum value of a corresponding portion of the error diffused halftone digital signal (b) when the prediction error signal is greater than zero (step 510) ; replacing the predicted continuous tone signal with a maximum value of the corresponding portion of the error diffused halftone digital signal (b) if the prediction error signal is less than zero (step 510') ; and storing the average of previous error diffused signal and the prediction error signal. A method for lossless decompression of a run-length encoded signal, comprising: inputting a run-length encoded prediction error signal (step 1050); run length decoding the run-length encoded prediction error signal to form a prediction error signal (step 1100) ; generating a decoded digital signal from the prediction error signal and a predicted digital signal (b*) (step 1200-1600) ; generating a predicted continuous tone signal from previous decoded digital signals (step 1150); and generating the predicted digital signal (b*) from the predicted continuous tone signal (step 1150). The method of claim 5, wherein the step of generating the predicted continuous tone signal comprises averaging a plurality of portions of previous decoded digital signals. The method of claim 5, wherein the step of generating the predicted continuous tone signal comprises: averaging previous decoded digital signals; determining a weighted sum of previous prediction error signals; and summing the average of previous decoded digital signals and the weighted sum of previous prediction error signals. The method of claim 7, further comprising: replacing the predicted continuous tone signal with a minimum value of a corresponding portion of the decoded digital signal when the prediction error signal is greater than zero; replacing the predicted continuous tone signal with a maximum value of a corresponding portion of the decoded digital signal when the prediction error signal is less than zero; and storing the averaged decoded digital signal and the predicted error signal. An apparatus for lossless compression of an error diffused halftone digital image signal, comprising: a prediction circuit (3) which inputs the error diffused halftone digital signal and outputs a predicted digital signal, comprising: an averaging circuit (3) which averages a plurality of portions of previous error diffused halftone digital signals and outputs a predicted continuous tone signal, and a predicted digital signal generator circuit (3) which inputs the predicted continuous tone image signal and outputs the predicted digital signal (b*) ; a prediction error circuit (4) which inputs the error diffused halftone digital signal and the predicted digital signal and outputs a prediction error signal; and a run length encoder (5) which inputs the prediction error signal and outputs a run length encoded prediction error signal.

Inventors: FAN ZHIGANG, LIN YING-WEI, FAN, ZHIGANG, LIN, YING-WEI
Applicants: XEROX CORP, XEROX CORPORATION
Application year: 1995
Main classification: H04N1
IPC codes: H03M7, H04N1, G06T9