Noise shaping predictive coding system

Abstract: A noise-shaping predictive coding system is disclosed, in which the deviation (i.e. correlation) of quantization representative values, which occurs when the coding noise resulting from predictive coding is frequency-shaped by a noise shaping filter, is used to perform coding oper­ ation, so that the quantization entropy can be reduced simultaneously. The human visual characteristic may be combined with the deviation of quantization representative values.

Description: The present invention relates to a predictive coding method which performs high efficiency coding of image signals as of commercial television or television conference system through utilization of high correlation between adjacent picture elements in a picture frame and, more particularly, to a coding method employing noise shaping by which noise resulting from predictive coding is shaped into noise which cannot easily be recognised visually. With the existing standard television system, a picture is formed by frames which are transmitted at a rate of 30 per second, and each frame has two continuous fields because of interlaced scanning every other scanning line. The elements which constitute the picture frame are called "picture elements"; since in the present specification digital processing is taken into account, one sample obtained by sampling will hereinafter be referred to as a picture element. Accordingly, in this instance the position of each picture element in the picture frame depends on the sampling frequency for digitizing signals. To reduce the required number of transmission bits for transmitting information of each picture element, quantization is generally employed. In the reduction of the required number of transmission bits by nonlinear or linear quantization, an increase in the quantization step size will decrease the number of bits but, at the same time, it will cause an increase in the amount of coding noise which is superimposed on coded image signals. Accordingly, in the case of obtaining a coded image as faithful to the original image as possible and of low coding noise, the quantization step size cannot be selected very large, and hence a substantial reduction of the required number of transmission bits is difficult. To solve this problem, permit a sharp reduction of the required number of transmission bits and implement a high quality coded image, the prior art has utilized a noise shaping filter in predictive coding. However, conventional noise shaping predictive coding system using a noise shaping filter has a defect such as increase of quantization entropy. It is therefore an object of the present invention to provide a noise-shaping, predictive coding method which retains the improved picture quality obtained by means of the noise shaping filter while at the same time permits the reduction of the quantization entropy. According to the present invention there is provided a noise-shaping, predictive coding method comprising using information of already coded picture elements to obtain a predicted value of a next picture element to be coded; obtaining a prediction error value between an input picture element and said predicted value; obtaining a noise shaping filter value for compensating for said prediction error value from information of quantizing noise; quantizing a difference value between the prediction error value and said noise shaping filter value to obtain a quantized value; and obtaining said information of quantized noise from a difference between said difference value and said quantized value; characterised by coding the quantized value for said next picture element by exploiting the mutual correlation between successive quantized values introduced by the noise shaping filter whereby there is a large probability that the quantized values of a just coded one of said already coded picture elements and said next picture element are of equal values and of opposite signs. Based on the fact that in employing the noise shaping filter, the quantized value has correlation between the current picture element (X i ) and the next subsequent one (X i+1 ), in embodiments of the present invention quantized values of several picture elements are consolidated into a block for block quantization or coding is performed by adaptively switching a code which is assigned to the next quantized value on the basis of that of the immediately preceding picture element (which coding will hereinafter be referred to as "sort quantization"). Embodiments of the present invention will now be described, by way of example, in comparison with prior art with reference to the accompanying drawings, in which: Fig. 1 is a diagram showing the positional relationships of picture elements for explaining the conventional predictive coding; Fig. 2A is a block diagram of a conventional noise-shaping, predictive coding system; Fig. 2B is its equivalent circuit diagram; Fig. 2C is an equivalent circuit diagram of a conventional predictive coding system; Fig. 3 is a diagram showing quantization representative value variation characteristics for explaining the principle of the present invention; Fig. 4 is a block diagram illustrating the noise-shaping, predictive coding system which employs block quantization according to an embodiment of the present invention, and Fig. 5 is a block diagram illustrating the noise-shaping, predictive coding system which employs sort quantization according to an embodiment of the present invention. Fig. 1 shows the positional relationships of picture elements 1 to 8, for explaining a conventional predictive coding system. Now, since the sampling frequency f s is selected to be an integral multiple of the horizontal scanning frequency, the picture elements are arranged in a matrix form, with lines of the preceding field FD₁ lying between lines of the current field FD₀ because of interlaced scanning. In this case, the picture element 2 lies at the left side of the current picture element 1 next thereto on the same line; the picture elements 3 and 4 lie just above the picture elements 1 and 2, respectively, one line above in the same field FD₀; the picture elements 5 and 6 lie just below the picture elements 1 and 2, respectively, one line below in the immediately preceding field FD₁; and the picture elements 7 and 8 lie at the same positions as the picture elements 1 and 2, respectively, in a field FD₂ one frame before the current field FD₀. In this instance, it can be considered that sample values of some closely spaced picture elements have high correlation. According to the prior art system, a prediction value X ₁ of the sample value X₁ of the picture element 1 is obtained in the following form, for example, using sample values of the adjoining picture elements: X ₁ = (3/4)X₂ + X₃ - (3/4)X₄ and a difference between this prediction value X ₁ and the true value X₁ ΔX₁ = X₁ - X ₁ is used as a prediction error, and by quantizing and coding this error, high efficiency coding is performed with a reduced number of bits needed to be transmitted. In general, quantization is divided into nonlinear quantization and linear quantization. In nonlinear quantization, since the width of the input range of the prediction error (the quantization step size) differs for each level number as shown below in Table 1, the transmission code used is usually a fixed-length code. On the other hand, in linear quantization, the quantization step size is fixed in common to the same all the level numbers as shown in Table 2 (which shows an example in which the quantization step is "5") and a variable-length code is employed, as the transmission code, whose code length gradually increases from the innermost or smallest level number toward the outermost or largest one on either side thereof, reflecting the property of the prediction error signal. Accordingly, the latter system is excellent from the viewpoint of reducing the required number of transmission bits, and high efficiency coding system usually employ linear quantization technique. In the reduction of the required number of transmission bits by nonlinear or linear quantization, an increase in the quantization step size will decrease the number of bits but, at the same time, it will cause an increase in the amount of coding noise which is superimposed on coded image signals. Accordingly, in case of obtaining a coded image as faithful to the original image as possible and of low coding noise, the quantization step size cannot be selected very large, and hence a substantial reduction of the required number of transmission bits is difficult. To solve this problem, permit a sharp reduction of the required number of transmission bits and implement a high-quality coded image, the prior art has utilized a noise shaping filter in predictive coding. Figs. 2A to 2C illustrate, by way of example, the arrangement of the transmitting side of the conventional predictive coding system employing a noise shaping filter (which system will hereinafter be referred to as the "noise-shaping, predictive coding system"), Fig. 2A being a block diagram and Fig. 2B an equivalent circuit. Fig. 2C shows, for reference, the circuit of a conventional predictive coding system with no noise shaping filter (which system will hereinafter be referred to as the "predictive coding system"). Arrangements such as shown in Figs. 2A to 2C are disclosed in "Proceedings of In. Conf. Commun." Amsterdam, The Netherlands, 1984 pp711-717 "Reconstruction Noise Shaping In The Context of Predictive TV Signal Coding". In Figs. 2A to 2C, reference numeral 20 indicate a predictor, 21 a prediction error generator which provides a difference between an input picture element value and a predicted value, 22 a prediction error compensator which corrects the prediction error value on the basis of an output value from a noise shaping filter 26 described later, 23 a prediction error quantizer which quantizes the corrected prediction error, 24 a quantizing noise detector which detects quantizing noise which is obtained from the difference between the quantized value and the prediction error value, 25 a memory for storing the detected quantizing noise signal, 26 a noise shaping filter which shapes the frequency characteristic of the quantizing noise, 27 an image decoder which decodes the input picture value from the quantized prediction error value and the predicted value, 28 a memory for storing a television signal, and 29 a transmission signal encoder which provides the quantized prediction error value on a transmission line. The block diagram of Fig. 2A, with the prediction error compensator 22, the quantizing noise detector 24, the memory 25 and the noise shaping filter 26 removed therefrom, is the same as the block diagram of the conventional predictive coding system and its equivalent circuit is shown in Fig. 2C. In this noise-shaping, predictive coding system a noise shaping filter value is produced on the basis of information about quantizing noise produced in an already coded image, and prediction error compensation is performed. The receiving side of the noise-shaping, predictive coding system is exactly identical in construction with the receiving side of the predictive coding system which does not involve noise shaping. Next, a description will be given of the effectiveness of the noise-shaping, predictive coding system and the problem to be settled. In the noise-shaping predictive coding system, a coding noise n i which is a difference signal between an i-th (where i = O to M) input image X i and its decoded image X i is given by Eq. (4) from Eq. (3). X i = X i - Σh j q i-j + q i n i = -Σh j q i-j + q i where q i is quantizing noise occurring in the quantizing operation of the image X i and h j (where j = 1 to ℓ) is the coefficient of the noise shaping filter which acts on quantizing noise q i-j . The power spectrum N(wx, wu, wt) of this coding noise in its frequency region is given by Eq. (5). N(wx, wy, wt) = |1 - H(wx, wy, wt)|² Q(wx, wy, wt) In the above equation, H(wx, wy, wt) represents the transfer function (A Fourier-transformed value of h i ) of the noise shaping filter and Q(wx, wy, wt) represents the power spectrum of the quantizing noise. Incidentally, the power spectrum of predictive coding noise, in case of employing no noise shaping filter, is Q(wx, wu, wt), and since this is white noise, its spectrum has a fixed magnitude regardless of frequency. On the other hand, studies of human visual characteristics reveal that noise having low-frequency components is more recognizable visually than noise having high-frequency components. Based on this fact, the coding noise which is originally white noise, as indicated by Eq. (5), is frequency shaped by the noise shaping filter into noise which fits the human visual characteristics. This ultimately makes the coding noise hard to be recognized visually, and hence equivalently improves the coded picture quality. The above is the effectiveness of the noise-shaping filter. The power E[e i ²] of a compensated prediction error signal e i which is applied to the quantizer is obtained by the following equation. E[e i ²] = E[(x i - x i )²] + Σh i ²E[q i ²] where x and i is a predicted value of the input image x i and E[ ] is an expected value. As indicated by Eq. (6), the noise-shaping, predictive coding system increases the power by Σh i ² E[q i ²] as compared with the ordinary predictive coding which does not involve noise shaping, and the noise shaping filter increases the amount of information. In connection with this increase in the amount of information, it is well-known that in case of employing a one-dimensional, first-order high-pass-type noise shaping filter (H(W x ) = ∫h i exp(-jw x t))dt), the amount of information increases by 0.3 to 0.5 bits in terms of the entropy of a quantization representative value. The inclusion of the noise shaping filter 26 has the defect that the required number of transmission bits increases as many as about 4 to 5 megabits. A description will be given now of the features of the present invention to attain the above object of the present invention and to eliminate the above defects of prior art. Table 3 is explanatory of the principle of the present invention, showing the transition characteristic of the quantization representative value obtained by simulation. As is evident from Table 3, when the quantization representative value of the current picture element (X i ) has a certain negative value, there is a very large probability that the quantization representative value of the next picture element (X i+1 ) is positive and equal in absolute value to the quantization representative value of the current picture element. The same is true of the case where the quantization representative value of the current picture element (X i ) is positive. The present invention is based on this characteristic, and the grounds for Table 3 will be described in detail. Fig. 3 is intended for describing the principle of the present invention in more detail and is a characteristic diagram of a quantization representative value changing region in case of using the noise shaping filter. In the interests of simplicity of the description, the following will describe a case of using a one-dimensional shaping filter (hq i-1 ). In Fig. 3, the vertical direction represents a prediction error signal e′ i which is provided from the one-dimensional shaping filter, without involving feedback, and the horizontal direction shows the one-dimensional shaping filter h(q i-1 ) in positive and negative regions. Reference characters R i-1 , R i and R i+1 indicate quantization representative values, and T i and T i+1 quantization determination values. Further, let it be assumed that S i and S′ i are given by the following equations. S i = T i+1 - |h(q i-1 )| S′ i = T i + |h(q i-1 )| In a case where the quantization representative value of the picture element X i varies owing to the feedback of the one-dimensional shaping filter h(q i-1 ), that is, where the noise shaping filter produces its effect, the quantization representative value varies in both regions A and B shown in Fig. 3. The other hatched region is one where the quantization representative value behaves as in the case of ordinary DPCM in which the quantization representative value undergoes no variation. In the region A, the quantization representative value changes from R i to R i+1 in a case where S i ≦ e′ i ≦ T i+1 and the one-dimensional shaping filter h(q i-1 ) is negative. The quantizing noise q i and the coding noise n i which are superimposed on the picture element X i in this instance are expressed by the following equations. q i = R i+1 - (e′ i - h(q i-1 )) > 0 n i = -h(q i-1 ) + q i > 0 From the both equations the coding noise n i always becomes a positive value and is superimposed on the picture element X i . Moreover, in the picture element X i-1 a positive quantizing noise (hq i ) is fed back, as a difference signal, for the prediction error signal e′ i+1 , with the result that the quantization representative value of the picture element X i+1 is very likely to become negative. In the region B, the quantization representative value changes from R i to R i-1 in a case where T i ≦ q′ i ≦ S′ i and the one-dimensional shaping filter h(q i-1 ) is positive. In consequence, the coding noise n i and the quantizing noise q i which are superimposed on the picture element X i both assume negative values as opposed to the case of the region A. In this instance, there is strong probability that the quantization representative value of the picture element X i+1 becomes positive. Table 3 shows the characteristics of quantized values of the picture elements X i and X i+1 obtained by simulation using real image data, and the results of simulation support the above. These results shown in the table were obtained with a one-dimensional shaping filter having a coefficient h₁ = 5/8 for intra-field predictive, linear quantization (using a step size of 11/256). As shown in Table 3, when the quantization representative value of the picture element X i is +11, the probability of the quantization representative value of the picture element X i+1 becoming -11 is higher than zero, whereas when the former is -11, the probability of the latter becoming +11 is high. In view of the above-described deviation or so-called correlation of the quantization representative value which results from the use of the noise shaping filter, the present inventors considered that the quantization entropy could be reduced simply by slightly modifying the conventional noise-shaping, predictive coding system through utilization of the marked correlation. Fig. 4 illustrates in block form a first embodiment of the present invention which applies the block quantization technique of the present invention to the conventional noise-shaping, predictive coding system. The embodiment depicted in Fig. 4 differs from the conventional arrangement in that a blocking section 33 is provided, at a stage preceding the transmission signal encoder 29, for consolidating quantization representative values of several picture elements into a block, to which codes are directly assigned. For example, in a case where the number of quantization representative values for a block consisting of quantized values of two picture elements is 8, 8² = 64 kinds of codes must be prepared for coding this block. In this instance, however, the number of coded bits necessary for transmitting the quantization representative value could be reduced by variable-length coding which assigns the shortest possible code to a combination of quantization representative values of high probability of occurrence and a long code to a combination of quantization representative values of low probability of occurrence through utilization of the above-mentioned quantization representative value variation characteristic by the noise shaping filter on which the present invention is based. While in the above it is effective for reducing the number of coded bits to constitute one block with two to four picture elements, the number of picture elements which are consolidated into one block varies with the constitution of the noise shaping filter, the quantization characteristic and the kind of an image to be handled (a moving or still picture). Fig. 5 illustrates in block form a second embodiment of the present invention in which the sort quantization technique of the present invention is applied to the conventional noise-shaping, predictive coding system. The embodiment of Fig. 5 differs from the conventional structure in substituting the transmission signal encoder 29 with a sequential encoder 34. In the sequential adaptive encoder 34 the quantization representative value of the precoded immediately preceding picture element is used to determine the code which is assigned to the quantization representative value of the next picture element and sequential coding is performed using the thus determined code. For instance, where the number of quantization representative values is eight, eight kinds of codes are prepared. In this case, however, coding appropriate to the probability of occurrence of the quantization representative value can be achieved by performing sequential variable-length coding whereby when the quantization representative value of the immediately previous picture element is negative, a short or long code is assigned to the quantization representative value of the next picture element depending on whether the latter is positive or negative, through utilization of the afore-mentioned quantization representative value variation characteristic by the noise shaping filter on which the present invention is based. In this way, the number of coding bits can be reduced. As described above, the present invention is intended to decrease the quantization entropy by performing coding through use of a correlation between the quantization representative values. Although the above embodiments have been described in terms of hardware, the invention can be implemented by changing software in the conventional system. Table 4 shows the reduction of the quantization entropy obtained by simulation conducted under the same conditions as mentioned previously. As shown in Table 4, according to the second embodiment, the amount of information can be reduced by about 0.1 bit per picture element as compared with the prior art arrangement, and a reduction of approximately 1.7 megabits can be achieved in terms of the overall television signal.

Claims: A noise-shaping, predictive coding method comprising: using information of already coded picture elements (x i-n ) to obtain a predicted value of a next picture element (x i ) to be coded; obtaining a prediction error value (e i =x i -x i ) between an input picture element (x i ) and said predicted value (x i ) ; obtaining a noise shaping filter value (Σh j q i-j ) for compensating for said prediction error value (e i ) from information of quantizing noise (q i-j ) ; quantizing a difference value between the prediction error value (e i ) and said noise shaping filter value (Σh j q i-j ) to obtain a quantized value, and obtaining said information of quantized noise (q i ) from a difference between said difference value and said quantized value; characterised by coding the quantized value for said next picture element exploiting the mutual correlation between successive quantized values introduced by the noise shaping filter whereby there is a large probability that the quantized values (R i-1 ,R i ) of a just coded one (x i-1 ) of said already coded picture elements and said next picture element (x i ) are of equal values and of opposite signs. A noise-shaping predictive coding method according to claim 1, characterised by consolidating said quantized values of several picture elements into blocks, each block including a just coded one of said already coded picture elements (x i-1 ) and said next picture element (x i ); and coding each block using a short or long code which is assigned in dependence on whether the probability of occurrence of the assigned code is high or low. A noise-shaping predictive coding method according to claim 1, characterised by coding the next picture element (x i ) by adaptively changing the code to be assigned to its quantized value on the basis of the quantization representative value of the already coded picture elements, in accordance with the correlation between the quantized values (R i-n , R i ) of the coded picture elements and the next picture element (x i ) to be coded.

Inventors: MATSUMOTO SHUICHI, MURAKAMI HITOMI, MATSUMOTO, SHUICHI, MURAKAMI, HITOMI
Applicants: KOKUSAI DENSHIN DENWA CO LTD, KOKUSAI DENSHIN DENWA KABUSHIKI KAISHA
Application year: 1988
Main classification: H04N7
IPC codes: H04N7