KR100203682B1 - Improved image coding system having functions for controlling generated amount of coded bit stream - Google Patents

Abstract

The present invention, in the coding system including MC-DCT, quantization, based on the time complexity between the previous image and the input image predicted based on the motion compensation error value and the spatial complexity of the previous frame reconstructed for motion estimation and compensation Calculates the complexity of the image to be encoded, and selectively removes high frequency components of the input video signal using a two-dimensional Discrete Fourier Transform according to the calculated result, thereby adaptively adjusting the amount of bits generated after encoding. The present invention relates to a video encoding system having a generation amount control function. To this end, the present invention calculates a spatial complexity value for each of the previous frames reconstructed for motion estimation and compensation, and then calculates the spatial complexity values of the respective previous frames. Averages the average spatial complexity values for a plurality of preset previous frames Calculates a motion compensation error value by adding each pixel value in units of macroblocks to an error signal, and then averages the motion compensation error values of each calculated macroblock, and then averages the motions of a plurality of preset frames. Time complexity calculation means for calculating an error value and calculating a final complexity value based on the calculated average spatial complexity value and the motion average error value; The calculated final complexity value is referred to as the complexity of the frame to be currently encoded through encoding means having DCT, quantization, and entropy encoding, and a preset plurality for adaptively limiting the frequency passband bandwidth of the current frame input for encoding. Control means for generating a bandwidth determination signal corresponding to the final complexity value calculated among the bandwidth determination signals of? And a high-pass passband for the two-dimensional DFT transform coefficient blocks in the frequency domain of the M × N block unit by using a two-dimensional discrete Fourier transform of the spatial domain image signal with respect to the input current frame signal. Converting into dimensional DFT transform coefficients, limiting the high pass band of the transformed 2D DFT transform coefficient block to the determined bandwidth based on the generated bandwidth determination signal provided from the control means, and quantizing the quantized DFT blocks Performing a inverse discrete Fourier transform on each to recover the original signal before encoding, and including a frequency selecting means for providing the encoding means as a current frame signal for motion estimation and compensation to the bandwidth limited frame signal restored to the original signal, Effect of bit amount generated after encoding without excessive step size increase in quantization by encoding means Can be adjusted by

Description

Image Coding System with Bit Rate Control

1 is a block diagram of a video encoding system having a bit generation amount adjusting function according to a preferred embodiment of the present invention.

2 shows a decision region for high frequency component limitation determined as an example, based on its complexity, for an 8x8 pixel block in accordance with the present invention.

* Explanation of symbols for main parts of the drawings

100, 170: frame memory 110: subtractor

120: image coding block 130: entropy coding block

140: transmission buffer 150: video decoding block

160: adder 180: current frame prediction block

210: time, space complexity calculation block 220: control block

230: frequency selection block

The present invention relates to a video encoding system for compressing and encoding a video signal. More particularly, the present invention relates to a video encoding system based on a motion compensation error value when compression encoding a video signal using a motion compensation differential pulse code modulation (MC-DPCM) technique. Adapts the generated bit amount after encoding with reference to the complexity of the input video signal predicted based on the temporal variation between the predicted previous image and the input image and the spatial complexity of the previous frame reconstructed for motion estimation and compensation. The present invention relates to an image encoding system having a bit generation amount adjustment function suitable for adjusting the amount.

As is well known in the art, the transmission of discrete video signals can maintain better image quality than analog signals. When a video signal consisting of a series of image "frames" is represented in digital form, a significant amount of data must be transmitted, especially for high quality televisions (aka HDTVs). However, since the usable frequency range of the conventional transmission channel is limited, in order to transmit a large amount of digital data, it is necessary to compress the transmitted data and reduce the amount thereof. Among the various compression techniques for compressing data, hybrid coding techniques combining probabilistic coding and temporal and spatial compression are known to be the most efficient, and these techniques are already established by the World Standards Organization. It is widely disclosed in recommendations such as MPEG-1 and MPEG-2.

Most hybrid coding techniques use motion compensated DPCM (differential pulse code modulation), two-dimensional DCT (discrete cosine change), quantization of DCT coefficients, VLC (variable length coding), and the like. The motion compensation DPCM determines a motion of an object between a current frame and a previous frame, and predicts a current frame according to the motion of the object to generate a difference signal representing a difference between the current frame and a predicted value. This method is described, for example, in Staffan Ericsson's “Fixed and Adaptive Predictors for Hybrid Predictive / Transform Coding”, IEEE Transactions on Communication, CCM-33, NO.12 (December 1985), or “A motion Compensated” by Ninomiy and Ohtsuka. Interframe Coding Scheme for Television Pictures ”, IEEE Transactions on Communication, COM-30, NO.1 (January, 1982).

In general, two-dimensional DCT converts digital image data blocks, for example, 8x8 blocks, into DCT conversion coefficients by using or eliminating spatial redundancy between image data. This technique is described in Chen and Pratt's “Scene Adaptive Coder”, IEEE Transactions on Communication, COM-32, NO.3 (March 1984). The DCT conversion coefficient may be processed through a quantizer, zigzag scan, VLC, etc. to effectively reduce (or compress) the amount of data to be transmitted.

More specifically, the motion compensation DPCM predicts the current frame from the previous frame according to the estimated motion of the object between the current frame and the previous frame. The estimated motion may be represented by a two-dimensional motion vector representing the displacement between the previous frame and the current frame.

Typically, there are several approaches to estimating the displacement of an object. These are generally classified into two types, one of which is a block-based motion estimation method using a block matching algorithm, and the other is a pixel-based motion estimation method using a pixel circulation algorithm.

In the motion estimation method for estimating the displacement of an object as described above, the displacement is obtained for each pixel by using the pixel-based motion estimation method. This method has the advantage of being able to estimate pixel values more accurately and easily handle scale changes (e.g., zooming, a movement perpendicular to the image plane), while the motion vectors are calculated for each pixel. Since it is determined, it is impossible to transmit substantially all the motion vectors to the receiver as large amounts of motion vectors occur.

In addition, in block-by-block motion estimation, an optimal matching block having a minimum error value is determined by comparing blocks with a corresponding pixel while moving a block by a pixel in a search range of a previous frame to a predetermined size of a current frame. From this, the interframe displacement vector (the extent to which the block has moved between frames) for the entire block is estimated for the current frame to be transmitted. Here, in determining the similarity between two corresponding blocks between the current frame and the previous frame, the average absolute difference, the mean square difference, etc. are mainly used, as is well known in the art.

On the other hand, the image bit stream encoded by the encoding technique as described above, that is, encoding techniques such as motion compensation DPCM, two-dimensional DCT, DCT coefficient quantization, and VLC (or entropy encoding) is transmitted to the output side of the image encoding system. The next transmission point stored in the buffer is sent to the transmitter for transmission to the remote destination. At this time, the transmission time point here is related to the size (ie, capacity) and transmission rate of the transmission buffer, and is controlled so that a malfunction (data overflow or data underflow) does not occur in the transmission buffer.

More specifically, various factors (e.g., image complexity) may cause a different amount of bits to be generated for each frame at the time of encoding. In view of this, in an image encoding system, the average bit rate may be kept constant. Control of the output buffer. That is, the video encoding system checks the bit generation amount up to the frame currently encoded based on the data fullness state information of the output transmission buffer and adjusts the bit amount to be allocated in the current frame. In other words, in the conventional typical video encoding system, the amount of bits generated in the encoding system is adjusted by controlling the quantization step size (QP) substantially based on the data full state information of the output transmission buffer, that is, if the amount of bits generated before has been large, The bit generation amount is controlled by reducing the bit generation amount by adjusting the step size largely, and in the opposite case, by adjusting the quantization step size small to increase the bit generation amount.

However, as described above, the conventional method of adjusting the bit generation amount by adjusting the quantization step size based on the data fullness state information of the output side transmission buffer is performed when encoding and transmitting the video data corresponding to each frame at the same data rate. In the case where the image to be encoded is complex (a large amount of high frequency components are generated), a large amount of bits is generated, which causes a problem that the quantization step size becomes large, resulting in serious image quality degradation in the reproduced image. The high frequency component generated here is a component that is substantially insensitive to human visual characteristics (a component that hardly affects the image quality of a reproduced video).

Accordingly, the present invention is to solve the problems of the prior art described above, in the coding system including MC-DCT, quantization, time complexity and motion estimation between the previous image and the input image predicted based on the motion compensation error value And calculating the complexity of the image to be currently encoded based on the spatial complexity of the previous frame reconstructed for compensation, and selectively removing the high frequency components of the input video signal using the two-dimensional discrete Fourier transform according to the calculation result. It is an object of the present invention to provide a video encoding system having a bit generation amount adjustment function capable of adaptively adjusting the bit generation amount after encoding.

In order to achieve the above object, the present invention provides a discrete cosine transform, quantization, and an error signal between an input current frame and a prediction frame obtained through motion estimation and compensation in units of macroblocks using the current frame and the reconstructed previous frame. Compression encoding is performed through encoding means including entropy encoding to generate an encoded bit stream, and the quantization has a bit generation amount adjusting function of adjusting a step size based on the fullness information of the bit stream stored in an output buffer. In the image encoding system, a spatial complexity value is stretched and contracted for each of the previous frames reconstructed for the motion estimation and compensation, and then averaged for each of the plurality of previous frames that are respectively compressed. Calculate the mean spatial complexity value A motion compensation error value is calculated by adding each pixel in units of macroblocks to the error signal, and then averaged motion compensation error values of each calculated macroblock. A time complexity calculation means for calculating a value and calculating a final complexity value based on the calculated average spatial complexity value and the motion average error value; And a plurality of predetermined bandwidth determination signals for referring to the calculated final complexity value as the complexity of the frame to be currently encoded by the encoding means, and for adaptively limiting the frequency passband bandwidth of the current frame input for encoding. Control means for generating a bandwidth determination signal corresponding to the calculated final complexity value among the signals; And converting the image signal in the spatial domain with respect to the input current frame signal into two-dimensional DFT transform coefficients in the frequency domain in M × N block units using a two-dimensional discrete Fourier transform, and generating the bandwidth provided from the control means. And forming a high frequency pass band for the transformed 2D DFT transform coefficient blocks based on the determined signal, limiting the high pass band of each transformed 2D DFT transform coefficient block to the determined bandwidth and limiting the bandwidth. Inverse discrete Fourier transform is performed on each of the quantized DFT blocks to restore the original signal before encoding, and the bandwidth limited frame signal reconstructed as the original signal is transmitted to the encoding means as a current frame signal for the motion estimation and compensation. With a bit generation amount adjustment function, characterized in that it further comprises a frequency selection means for providing Provided is an image encoding system.

The above and other objects and various advantages of the present invention will become more apparent from the preferred embodiments of the present invention described below with reference to the accompanying drawings by those skilled in the art.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a block diagram of a video encoding system having a bit generation amount adjusting function according to a preferred embodiment of the present invention. As shown in the figure, the image encoding system of the present invention includes a first frame memory 100, a subtractor 110, an image encoding block 120, an entropy encoding block 130, a transmission buffer 140, and image decoding. Block 150, adder 160, second frame memory 170, current frame prediction block 180, temporal and spatial complexity calculation block 210, control block 220, and frequency selection block 230. do.

Referring to FIG. 1, the input current frame signal is input to the next frequency selection block 230 stored in the first frame memory 100, and the frequency selection block 230 is provided from a control block 220 to be described later. Adapt the frequency of the input frame signal according to the control signal (bandwidth decision signal for frequency domain classification) calculated based on the spatial complexity of the previous frame reconstructed for motion estimation and compensation and the time complexity of the image according to the motion compensation error value In general, the limitation, that is, the high frequency component (which is a relatively insensitive part of the human eye) of the input image is removed by using the two-dimensional DFT. Reference will be made later in detail. Then, the current frame signal in which the high frequency component is adaptively removed is provided to the subtractor 110 and the current frame prediction block 180 through line L11, respectively.

First, the subtractor 110 performs a motion compensated predicted current for a moving object provided from the current frame prediction block 180 described below via line L19 from the current frame signal provided by the frequency selection block 230 via line L11. The frame signal is subtracted, and as a result, an error signal representing data, that is, a difference pixel value, is generated on the line L12. The error signal on line L12 is then transformed into a series of quantized DCT transform coefficients by using discrete cosine transform (DCT) and one of the quantization methods well known in the art via image coding block 120. Is encoded. At this time, the quantization of the error signal in the image encoding block 120 is based on the step size based on the quantization parameter QP determined according to the data fullness state information provided from the output side transmission buffer 140 described later through the line L21. Is adjusted.

Further, according to the present invention, the error signal on the line L12 is provided to the time and space complexity calculation block 210, which will be described later, the time, space complexity calculation block 210 by adding the error signal on the line L12, A motion compensation error value is calculated, and the time complexity of the frame to be encoded is calculated using the calculated motion compensation error value. In the present invention, the time complexity calculated as described above and the spatial complexity of the reconstructed previous frame described below are calculated. Based on the final complexity calculated by combining the two-dimensional DCT using a two-dimensional DCT before the quantization step in the image coding block 120, a relatively insensitive to a high frequency component of the human being adaptively (or selectively) limited. Therefore, the present invention can appropriately adjust the quantization step size that causes deterioration of image quality in the reproduced video even in a complicated video accompanied by an increase in the encoded bit generation amount. A detailed process of adaptively limiting the bandwidth in the low pass filtering in the quantization step by using the bandwidth determination signal set based on the time complexity and spatial complexity information of the calculated image will be described later in detail. .

Next, the quantized DCT transform coefficients on line L13 are sent to entropy coding block 130 and image decoding block 150, respectively. Here, the quantized DCT transform coefficients provided to the entropy coding block 130 are encoded by, for example, variable length coding techniques, and provided to the transmission butter 140 on the output side. It is delivered to the transmitter not shown for transmission.

Meanwhile, the quantized DCT transform coefficients on the line L13 provided from the image coding block 120 to the image decoding block 150 are converted into a frame signal reconstructed again through inverse quantization and inverse discrete cosine transform, and then adder 160. The adder 160 adds the reconstructed frame signal from the image decoding block 150 and the predicted current frame signal provided from the current frame prediction block 180 described later through line L19 to reconstruct the previous image. A frame signal is generated, and the previous frame signal reconstructed as described above is stored in the second frame memory 170. Accordingly, the immediately previous frame signal for every frame encoded through this path is continuously updated, and the reconstructed previous frame signal thus updated is provided to the current frame prediction block 180 described later for motion estimation and compensation. .

In addition, the reconstructed reconstructed previous frame signal stored in the second frame memory 170 is provided to the spatial complexity calculation block 210 described below through line L16 for calculating the spatial complexity of the input frame according to the present invention. .

On the other hand, in the current frame prediction block 180, the current frame signal in which the high frequency component on the line L11 provided from the frequency selection block 230 according to the present invention is selectively removed or the high frequency component has not been removed and the second frame described above are described. In the preset search range (eg, 16 × 16, or 32 × 32 search range) of the previous frame reconstructed using the block matching algorithm based on the reconstructed previous frame signal on the line L15 provided from the frame memory 170. Predict the current frame in units of predetermined blocks (e.g., 8x8 or 16x16 DCT blocks) and then generate the predicted current frame signal on line L19 to adder 160 of subtractor 110 described above. Provide each. At this time, the switch SW on the line L19 is turned on / off according to the control signal CS from the system controller (not shown). When the switch SW is turned on, the current encoding mode is the inter mode. In contrast, when off, the current encoding mode is intra mode. Accordingly, the subtractor 110 provides an error signal between the current frame signal and the predicted frame signal to the image encoding block 120 during inter-mode encoding, and transmits the current frame signal itself to the image encoding block 120 during intra-mode encoding. to provide.

In addition, the current frame prediction block 180 generates a set of motion vectors for each of the selected blocks (8 × 8 or 16 × 16 blocks) on the line L17 and provides the above-described entropy coding block 130. Here, the sets of detected motion vectors are predicted in a block (8x8 or 16x16 block) of the current frame and a preset search area (e.g., 16x16 or 32x32 search range) in the previous frame. It is the displacement between the most similar blocks. Accordingly, in the entropy coding block 130 described above, the quantized DCT transform coefficients on the line L13 together with the sets of the motion vectors on the line L17 generate a coded bit stream by encoding, for example, through a variable length coding technique. .

Meanwhile, in the temporal and spatial complexity calculation block 210 according to the present invention, the average error AE of the temporal complexity of the input image based on the error signal between the current frame and the prediction frame obtained through the motion compensation between the current frame and the restored previous frame. (Average Error), and also calculate the spatial complexity ACT (Activity) of the previous frame for motion estimation and compensation, and then use these two complexity values (AE, ACT) to adaptively apply the high frequency components before the quantization step. Generate complexity C (Complexity) to generate weights that decide to remove. As described above, when the complexity of the input image is calculated by referring to both the complexity in the time domain and the complexity in the spatial domain, the amount of computation is higher than that of only the complexity in one domain (complexity in the time domain or spatial domain). It may be somewhat larger, but it is possible to calculate the complexity of the input image more accurately.

First, the spatial and spatial complexity calculation block 210 calculates the spatial complexity of the image for the reconstructed previous frame provided from the second frame memory 170 through the line L16 to calculate the spatial complexity value ACT. The spatial complexity related to the amount of information of the video signal to be encoded is calculated using the variance value (standard deviation) of the video signal, and the spatial complexity of the previous frame thus calculated is referred to as the image complexity of the frame to be currently encoded. The present invention adaptively (or filters) the high frequency component which is relatively insensitive to human visual characteristics in the input production signal based on the spatial complexity of the previous frame and the time complexity described below.

In the present invention, the variance value (standard deviation) of the video signal is used when calculating the spatial complexity of the restored previous frame. In the case of the variance value used here, when the value is large, the result of performing the DCT is a high frequency component. The distribution of transform coefficients is inadequate for compressing the data because it will contain many (components with relatively insensitive human visual characteristics). A good image for ideal data compression is the case where there is no high frequency component but only DC component, and the transformed coefficient distribution has only one value at the position of (0,0). In addition, when the variance value is large, motion compensation may not be performed properly, and as a result, the structure of the motion compensated image is not suitable for encoding. Therefore, such spatial complexity can be interpreted as the amount of information of the video signal, that is, the amount of data to be encoded and transmitted.

For example, assuming that one frame has a size of M × N, Ip is a picture of a previous frame reconstructed and reconstructed after encoding, and Ic is a picture of a current frame input for encoding. The pixel value of the Ip image at the x, y) position is Ip (x, y). In this case, the complexity of the currently input Ic image is calculated based on the spatial complexity (ACT) of the image that is calculated as in the following Equation 1 with respect to the image signal of the previous frame reconstructed after coding (DCT, quantization) for motion estimation and compensation ) Can be used.

In the above formula (1), MIp means an average value for the reconstructed previous frame Ip image, and the average MIp for the Ip image is calculated as follows (2).

As described above, the reason for using the ACT value of the previous frame reconstructed as the complexity of the current frame to be encoded is that the characteristics of the video signal do not change rapidly every frame.

Therefore, it can be said that the calculated spatial complexity ACT value is related to the information amount (bit generation amount) of the image. If the current encoded image is complex, the calculated spatial complexity ACT value will be relatively large, and vice versa. In this case, the calculated spatial complexity ACT value will be small.

In view of this point of view, the spatial complexity ACT value between the reconstructed previous frames calculated at the time of encoding may be interpreted as a value related to the amount of information to be transmitted. In the calculation of the spatial complexity ACT value of the reconstructed previous frame according to the present invention, since the reconstructed previous frame signal required in the process of performing the motion estimation and compensation by the encoding system is stored in the frame memory, Using part of the coding system, one can easily obtain the spatial complexity ACT value of the desired reconstructed previous frame.

On the other hand, in order to calculate the time complexity, the spatial and spatial complexity calculation block 210 of the present invention calculates an error generated during the motion compensation process, and the motion compensation error value is a predetermined unit (motion estimation in all MPEG specifications). Is performed in blocks of 16x16 units (i.e., macroblocks), in which case the size of the search block in the previous frame is 32x32 units from the previous frame reconstructed using the motion vectors detected by the motion vector. It can be calculated by adding the error signal between the predicted current frame signal on the line L19 obtained by performing the motion compensation and the current frame signal on the line L11.

For example, when the reconstructed image of the previous frame is called Ip and the image of the currently input frame is called Ic, the value of each pixel at the position of (x, y) is Ip (x, y), Ic (x, y) will be At this time, since motion estimation is performed in units of M × N blocks (for example, 16 × 16 macroblocks) as described above, MVX (i, j), Suppose MVY (i, j), the motion compensation error value E (i, j) for the i, j-th macroblock is calculated by the following equation (3). In this case, the motion bettor is a value already detected in the motion estimation process in the above-described current frame prediction block 180.

In the above Equation (3), L means the horizontal and vertical size of the macro block. If a video signal of one frame has M × N size and one macro block has L × L size, The number of macroblocks for each will be (M / L) × (N / L). For example, assuming 16 × 16 macroblocks for an input video signal of 352'288, the number of macroblocks is (352/16) × (288/16), which corresponds to 22 × 18, or 396. .

Next, in the time and space complexity calculation block 210, when the motion compensation error value for each macro block of one frame is obtained through the above-described process, the entire frame of the one frame is again expressed using Equation (4) below. An average error AE value for an image is calculated, and the average error AE value of one frame calculated here is obtained by averaging the motion compensation error value for the entire image.

In Equation (4), P and Q correspond to the number of macroblocks in the horizontal and vertical directions, respectively. Here, the average error AE value calculated using Equation (4) above may be a value that substantially reflects the complexity of the image data. Therefore, in the present invention, the average error AE value calculated by averaging the motion compensation error values of all such macro blocks is used as the complexity of the next video signal input for encoding. In this case, the calculated average error AE value may be regarded as related to the information amount (bit generation amount) of the image. If the current encoded image is complex, the calculated average error AE value will be relatively large, and vice versa. The calculated mean error AE value will be small.

In addition, when the average error AE value of each frame calculated in the process of encoding the video signal is zero, the decoding system at the receiving side can reproduce the current video signal using only the previously encoded and transmitted video. The system does not need to transmit the current image data. In view of this, the average error AE value of each frame calculated at the time of encoding may be interpreted as a value related to the amount of information to be transmitted. In calculating the average error AE value of each frame according to the present invention, since the encoding system stores the motion vector detected in the process of performing the motion compensation and the reconstructed previous frame signal in the frame memory. Using a part of, it is possible to easily implement the desired average error AE value by averaging the compensation error value E (i, j) of each macro block described above without any additional calculation.

Then, the spatial and spatial complexity calculation block 210 finally calculates the complexity C [C = (ACT + AE) / based on the spatial complexity ACT calculated through the above-described process and the motion error average value AE for the time complexity. 2] is provided to the next control block 220.

Meanwhile, the control block 220 generates a frequency bandwidth determination signal B for limiting the frequency of the input image on the line L23 based on the final complexity C value provided from the temporal and spatial complexity calculation block 210 to select a frequency. Provided to block 230. Here, the bandwidth determination signal B for region division generated and provided to the frequency selection block 230 limits the frequency band of the input frame signal. In this case, the bandwidth determination signal B necessary to classify the frequency domain is calculated by the following method, and the process of setting the frequency domain of the input image to be restricted according to the present invention using the bandwidth determination signal B is described in the appended Article. It will be described later in detail with reference to FIG. 2.

In more detail, the weight generation block 220 outputs the bandwidth determination signal B for region division using the complexity C value output from the spatial and spatial complexity calculation block 210 as shown in Equation 5 below.

The process of obtaining MC and SC in Equation (5) is as follows. That is, in the case where the frame rate of the video signal is 30, the average value of 30 C values calculated for one second is MC, and the standard deviation of the value is SC. Therefore, the area discriminating value B can be obtained by comparing the MC and SC values thus obtained with the C value generated in each frame. That is, the control block 220 determines the area division value B by using the average and standard deviation of the C values generated during the previous 30 frames. As a result, the currently generated C value obtained through this process is again used to find the mean value and standard deviation of 30 frames. Accordingly, the control block 220 discards the first C value (the oldest C value in time) among the C values of the 30 frames generated previously, and uses the newly input C value from the temporal and spatial complexity calculation block 210. To find the mean and standard deviation. Of course, the current C value is not used to calculate the mean and standard deviation after the 31st frame.

As is apparent from the above equation (5), the area discriminating value B has an integer value between 1 and 4, which is an error signal output from the subtractor 110 described above (the current frame on the line L11 and the prediction frame on the line L19). This is to adaptively adjust the bandwidth according to the C value in the two-dimensional low pass filtering before quantization of the DCT transform coefficients of the discrete cosine transform of the difference signal).

On the other hand, the frequency selection block 230, based on the bandwidth determination signal B for the frequency domain classification provided from the above-described control block 220 limits the high frequency components relatively insensitive to the time in the input image, the process is Substantially, it can be divided into two-dimensional frequency conversion process and frequency selection process. In this case, the two-dimensional frequency conversion process uses a two-dimensional DFT, and in the frequency selection process based on the bandwidth determination signal B provided from the control block 220 described above. A passband of the 2D DFT transformed video signal is determined.

Next, in the frequency selection block 230 as described above, the input image is two-dimensional DFT transformed, and the frequency of the two-dimensional DFT-converted image signal is selected based on the bandwidth determination signal B for frequency domain classification. This will be described in detail with reference to FIG. 2.

First, the frequency selection block 230 utilizes the similarity of the spatial domain of the input image signal. The frequency selection block 230 uses the Fourier function to convert the spatial domain image signal (pixel data) into M × N units, For example, two-dimensional DFT transform coefficients in a frequency domain of 8x8 units are converted.

In Equation (6), f (u, v) represents the value of each pixel, u represents the position in the horizontal direction, and v represents the position of the pixel in the vertical direction. Therefore, the value for each pixel of the N × N block has the following value. That is, when N = 8 u and v have a value between 0 and 7. At this time, each value has an integer value between 0 and 255. In addition, in Equation (6), Z (k, l) means a converted signal, and k, l means frequency components in the horizontal and vertical directions, respectively. Therefore, when N = 8, an 8x8 DFT block, that is, a signal in the spatial domain is converted into a signal in the frequency domain.

More specifically, in the frequency selection block 230, the bandwidth determination signal B for frequency domain division provided from the control block 220 of FIG. 1 via the line L23, for the DFT conversion coefficients obtained through the above-described process. Based on this, the frequency band that is passed is determined. As described above, since the bandwidth determination signal B for frequency domain division is an integer value between 1 and 4, the frequency selected according to this is as follows.

That is, the frequency selection block 230 selects a specific frequency from the converted frequency Z (k, l). Here, k, l is an integer value between 0 and N-1. Therefore, the value output from the frequency selection block 230 is a signal from which a specific frequency component (ie, a high frequency component) is removed. For example, in the case of N = 8, the pass frequency band will be determined as shown in FIG. 2 as an example.

As shown in FIG. 2, when the bandwidth determination signal B value for frequency domain division provided from the control block 220 of FIG. 1 to the frequency selection block 230 through the line L23 is 1, the conversion frequency Z (k, l) are all selected, and when the B value is 2, 3, or 4, as shown in FIG. 2, frequencies below the dotted line corresponding to each are not selected as the mode 0. That is, when the value of B in FIG. 2 is 4, frequencies below the dotted line such as Z (1,7), Z (2,6), etc. are all mapped to 0.

Next, as described above, the DFT transform coefficients (signals in the frequency domain) from which the frequency (high frequency component) of the specific region is removed according to the bandwidth determination signal B value determined based on the complexity of the image are shown below (7). Is converted back to the original spatial signal.

In Equation (7), f (u, v) denotes the value of each pixel, u and v denote the position of the pixel in the horizontal and vertical directions, and Z (k, l) denotes the converted signal. , k and l denote frequency components in the horizontal and vertical directions, respectively. Therefore, when N = 8, 8 × 8 DFT blocks in the frequency domain are converted into signals in the spatial domain.

As a result, in the frequency selection block 230, the subtractor 110 and the current frame prediction block 180 of FIG. 1 through the line L11 are applied to the image signal from which the frequency of the specific region is removed according to the image complexity, that is, the image complexity. According to the bandwidth determination signal B calculated on the basis of this, the video signal in which the high frequency component of the image is selectively (or adaptively) removed is provided.

Accordingly, in the image encoding block 120 of FIG. 1, in the case of a complex image, encoding (quantization) is performed in a state in which a high frequency component of an image relatively insensitive to human vision is selectively (or adaptively) removed as described above. Therefore, the low frequency signal, which is a visually important component, can be encoded with less quantization error. If the low pass bandwidth of the frequency is not limited as in the present invention despite the complicated image, as a result, the amount of bits generated after encoding increases and the quantization step size becomes large. A large number of quantization errors are generated, and as a result, image quality deterioration due to quantization will be caused in the playback image on the receiving side.

As described above, according to the present invention, the previous image predicted based on the spatial complexity information and the motion compensation error value calculated every frame using the previous frame reconstructed and reconstructed after encoding for motion estimation and compensation during encoding, The final complexity of the input image to be encoded is calculated by using the time complexity value information between the input images, and when the current input image is a complex image according to the final complexity calculation result, it is insensitive to human vision by giving a corresponding weight. By first removing high frequency components of an image and then performing encoding such as MC-DCT and quantization, the bit amount generated after encoding can be effectively controlled without increasing an excessive step size in the quantization step. Therefore, according to the present invention, it is possible to effectively reduce image quality deterioration due to quantization error inevitably present in a reproduced image when reconstructing and displaying an encoded image.

Claims (5)

Compression means including discrete cosine transform, quantization, and entropy encoding for the error signal between the input current frame and the prediction frame obtained through motion estimation and compensation in units of macroblocks using the current frame and the reconstructed previous frame. A video encoding system having a bit generation amount adjustment function of encoding and generating a coded bit stream, wherein the quantization is adjusted based on the full state information of the bit stream stored in an output buffer. Computing a spatial complexity value for each of the previous frames reconstructed for compensation, and then averaging the spatial complexity values of the respective previous frames to calculate an average spatial complexity value for a plurality of preset previous frames, the error Each macro block for a signal The motion compensation error value is calculated by adding each pixel value in units, and then the motion compensation error values of the respective macroblocks are averaged to calculate a motion average error value for a plurality of preset previous frames. Spatial complexity calculation means for calculating a final complexity value based on the spatial complexity value and the motion average error value; And a plurality of predetermined bandwidth determination signals for referring to the calculated final complexity value as the complexity of the frame to be currently encoded by the encoding means, and for adaptively limiting the frequency passband bandwidth of the current frame input for encoding. Control means for generating a bandwidth determination signal corresponding to the calculated final complexity value among the signals; And converting the video signal in the spatial domain with respect to the input current frame signal into two-dimensional DFT transform coefficients in the frequency domain in M × N block units using a 2D Discrete Fourier Transform, and the generated bandwidth provided from the control means. Determine a high frequency pass band for the transformed 2D DFT transform coefficient blocks based on a determination signal, limit the high pass band of each transformed 2D DFT transform coefficient block to the determined bandwidth, and limit the bandwidth Inverse discrete Fourier transform is performed on each of the quantized DFT blocks to restore the original signal before encoding, and the bandwidth limited frame signal reconstructed as the original signal is transmitted to the encoding means as a current frame signal for the motion estimation and compensation. With a bit generation amount adjustment function, characterized in that it further comprises a frequency selection means for providing Video encoding system. The method according to claim 1, wherein each of the predetermined band-cancer determination signals comprises: a first signal for a plurality of frames transmitted per second obtained by averaging the calculated average spatial complexity value of each frame and the average spatial complexity value of each frame. A second standard for a plurality of frames transmitted per second obtained by averaging an average error value, a first standard deviation of the first average error value, a motion average error value of each calculated frame, and a motion average error value of each frame And a second standard deviation of the second mean error value and the second mean error value. 3. The method of claim 1, wherein the plurality of predetermined bandwidth determination signals include four bandwidth determination signals each having a different integer value for selective bandwidth restriction of each of the two-dimensional DFT transform coefficient blocks. An image encoding system having a bit generation amount adjusting function. The image having a bit generation amount adjusting function of claim 1, wherein the frequency selecting means converts an image signal in a spatial domain with respect to the current frame signal into two-dimensional DFT transform coefficients in units of 8x8 blocks. Coding system. 5. The method of claim 1 or 4, wherein the frequency selecting means maps high frequency components below the generated bandwidth determination signal to zero values for each of the converted two-dimensional DFT transform coefficient blocks. An image encoding system having a bit generation amount adjusting function.