KR100203630B1 - 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 currently, and selectively removes high frequency components of the input video signal using a one-dimensional low-pass filter 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 reconstructed previous frame for motion estimation and compensation, and then averages the calculated spatial complexity values of each previous frame. Calculates average spatial complexity values for a plurality of preset previous frames A motion compensation error value is calculated by adding each pixel value in units of macroblocks to an error signal, and then averaging motion compensation error values of each calculated macroblock, respectively, to average motions for a plurality of preset frames. Time and space 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 by the encoding means having DCT, quantization, and entropy encoding, and a filter for limiting the frequency passband bandwidth of the current frame input based on the calculated final complexity value. Control means for generating a control signal; And based on the generated filter control signal, the input current frame is provided as a current frame signal for motion estimation and compensation without filtering to the coded signal, or the one-dimensional low pass filtering is applied to the input and current frame to limit the pass band. And a one-dimensional low-pass filtering means for providing the encoding means with the current frame signal for motion estimation and compensation by removing the high frequency component, thereby generating a bit after encoding without undue step size increase during quantization in the encoding means. The amount can be effectively controlled.

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 is an illustration of an 8x8 pixel block as an example in accordance with the present invention.

3 is an exemplary view showing a one-dimensional lowpass filter coefficient of order 7 as an example according to the present invention.

4 is an exemplary view showing a horizontal filtering process at (0,4) and a vertical filtering process at (3,0) as an example according to 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: filter control block

230: low pass filtering block

The present invention relates to a video encoding system for compression 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 in the case of high quality television (aka HDTV). 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 have been proposed by the World Standards Organization for example. It is widely disclosed in the recommendations of MPEG-1 and MPEG-2.

Most hybrid coding techniques use motion compensated DPCM (Differential Pulse Code Modulation) two-dimensional Discrete Cosine Transform (DCT), 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 can be used for example in Staffan Ericsson's Fixed and Adaptive Predictors for Hybrid Predictive / Transform coding, IEEE Transactions on communication, COM-33, NO.12 (1985, December), or A motion Compensated Interframe Coding from Ninomiy and Ohtsuka. Scheme for Television Pictures, IEEE Transactions on Communication, COM-30, NO. 1 (1982, January).

In general, two-dimensional DCT converts digital image data blocks, such as 8x8 blocks, into DCT conversion coefficients by using or removing spatial redundancy between image data. The technique is described in Chen and Pratt's Scene Adaptive Coder, IEEE Transactions on Communication, COM-32, NO.3 (1984, March). 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 motion of the object estimated 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 pixel-based motion estimation method using a block matching algorithm, and the other is a pixel-based motion estimation method using a pixel cycling 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, which is a movement perpendicular to the image plane), while the motion vectors for each pixel Since it is determined, it is impossible to send substantially all the motion vectors to the receiver as the motion vectors occur.

In addition, in block-by-block motion estimation, a block having a predetermined size of the current frame is moved by one pixel in a search range of a previous frame and compared with the corresponding blocks to determine an optimal matching block having a minimum error value. 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, for 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 buffer of the image encoding system. When the next transmission point is stored at the transmitter, it is sent to the transmitter for transmission to the remote receiver. 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, resulting in a large quantization step, 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 current image to be 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 a one-dimensional low pass filter 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. A coded bit stream is encoded and encoded through an encoding means including entropy encoding, and the encoded bit stream is generated, and the quantization has a bit generation amount adjusting function of adjusting a step size based on the full state information of the bit stream stored in an output buffer. In an image encoding system, a spatial complexity value is calculated for each of the previous frames reconstructed for the motion estimation and compensation, and then the averaged spatial complexity values of the respective previous frames are calculated for each of a plurality of preset previous frames. Calculate the average spatial complexity value A motion compensation error value is calculated by adding each pixel value to the error signal in units of macroblocks, and then averages the motion compensation error values of the respective macroblocks, respectively. 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 by the encoding means, and a filter control for limiting the frequency passband bandwidth of the current frame input for encoding based on the calculated final complexity value Control means for generating a signal; And based on the generated filter control signal, provide the input current frame as the current frame signal for motion estimation and compensation without filtering to the encoding means or apply one-dimensional low pass filtering to the input current frame. And a one-dimensional low pass filtering means for providing a frame signal from which high frequency components are removed by limiting a pass band to the encoding means as a current frame signal for motion estimation and compensation. Provide an 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 an image encoding. Block 150, adder 160, second frame memory 170, current frame prediction block 180, temporal and spatial complexity calculation block 210, filter control block 220 and low pass filtering block 230 It includes.

Referring to FIG. 1, the input current frame signal is stored in the first frame memory 100 and then input to the low pass filtering block 230, and the low pass filtering block 230 includes the filter control block 220 described later. The frequency of the input frame signal according to a control signal (a logic signal of level 0 or 1) 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 Is selectively filtered, that is, one-dimensional low pass filtering is used to remove high frequency components (which are insensitive to comparative human vision) of the input image. For the specific operation of the low pass filtering block 230, It will be described later in detail with reference to FIG. Then, the current frame signal from 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 motion compensated predicted motion on the moving object provided from the current frame prediction block 180 described later through line L19 from the current frame signal provided by the low pass filtering block 230 via line L11. The current 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 encoded 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. do. 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 and 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 calculated time complexity and the spatial complexity of the restored previous frame are described. Based on the final complexity calculated by using the 1D low pass filtering, the high frequency component relatively insensitive to the visual characteristics of the seal in the input image is 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 image thus calculated will be described in detail later.

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 buffer 140 at the output side, and the encoded image signal is transmitted to the receiving 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 reconstructed frame signals 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 in this way 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 a space and time complexity calculation block 210 described below through line L16 for calculating the space 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 low pass filtering block 230 according to the present invention is selectively removed or the high frequency component is not removed and the above-described product is removed. In a 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 two-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 the subtractor 110 and adder 160 described above. Provide each. In this case, the switch SW on the line L19 is turned on / off according to the control signal CS from the system controller (not shown), and when the switch SW is on, the current encoding mode is an inter mode. On the contrary, when off, this means that the current encoding mode is an 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 18x8 generates a set of motion vectors for each selected block (8x8 or 16x16 blocks) on line L17 and provides it to the entropy coding block 130 described above. 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. Thus, in the entropy coding block 130 described above, the quantized DCT transform coefficients on the line L13 together with the sets of motion vectors on the line L17 are encoded through, for example, a variable length coding technique. To generate an encoded bit stream.

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 somewhat higher than that of only the complexity in one domain (complexity in the time domain or spatial domain). It may be 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 an image signal to be encoded is calculated using the variance value (standard deviation) of the 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. Based on the spatial complexity of the previous frame and the temporal complexity described below, the high frequency component relatively insensitive to human visual characteristics is adaptively (or filtered) removed from the input image signal.

In the present invention, the variance value (standard deviation) of the video signal is used when calculating the spatial complexity of the reconstructed previous frame. In the case of the variance value used here, the value of 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 distribution of the transformed coefficient 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 using the spatial complexity (ACT) of the image calculated as shown in Equation 1 below for 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 value MIp for this 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 frame to be currently 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. The resulting spatial complexity ACT value will be small.

In view of this aspect, the spatial complexity ACT value between the restored 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, such encoding is performed. Using part of the system, one can easily obtain the spatial complexity ACT value of the desired restored 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 a block of 16 × 16 units, i.e., a macro block, in which case the size of the search block in the previous frame is 32 × 32 units). The error signal between the predicted current frame signal on the line L19 obtained by performing the compensation and the current frame signal on the line L11 can be calculated.

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), If 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 vector is a value already detected in the motion estimation process in the current frame prediction block 180 described above.

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 a 352 × 28 × 8 input video signal, the number of macroblocks is (352/16 × 16 (288/16), which corresponds to 22 × 18, or 396. do.

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. The average error AE value for the 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 the above Equation (4), P and Q are values corresponding to the number of macro blocks in the horizontal and vertical directions, respectively. Here, the average error AE value calculated using Equation (4) 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 considered to be related to the information amount (bit generation amount) of the image. If the current encoded image is complicated, the calculated average error AE value will be relatively large, and vice versa. The mean error AE value will be smaller. 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 point of view, 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 the calculation of 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 values E (i, j) of each macro block described above without the need for further calculations.

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 filter control block 220 of the next stage.

Meanwhile, the filter control block 220 generates a filter control signal for performing one-dimensional filtering 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 pass the low pass. Provided to the filtering block 230, that is, in the filter control block 220, if the final complexity C value input from the spatial and spatial complexity calculation block 210 is greater than a predetermined threshold value, a high level logic signal is generated to generate a low pass. If the final complexity C value is smaller than the predetermined threshold value, the low level logic signal is generated and provided to the low pass filtering block 230. In this case, the preset threshold value may be preferably set to an average value of the final complexity C value generated over a predetermined time during the encoding process. As an example, when the frame rate of the video signal is 30, 30 C values calculated for 1 second may be averaged and compared with this value and the C value generated every frame. That is, when the average value of the C value generated during the previous 30 frames is B, in the filter control block 220, if the C value generated in the current frame is larger than the average value B, the high level logic is used as the filter control signal on the line L23. A signal is generated, and when small, a low level logic signal is generated as the filter control signal on the line L23. Of course, the average value B of C for the previous 30 frames will be continuously updated for the C value for every frame that is generated, at which time the oldest C value of the C values for the previous 30 frames is discarded.

Meanwhile, the low pass filtering block 230 passes the input video signal as it is, based on the filter control signal (high or low level logic signal) from the filter control block 220 (that is, the subtractor through the line L11). (110) and the current frame prediction block 180) or by applying one-dimensional low pass filtering to the input video signal to limit the pass band, thereby removing the high-frequency components relatively insensitive to human visual characteristics Occurs on line L11. That is, the low pass filtering block 230 removes high frequency components from the input image through one-dimensional low pass filtering when the filter control signal on the line L23 is a high level logic signal, and the filter control signal on the line L23 is low level logic. In the case of a signal, a high frequency component is removed from the input image without being removed.

More specifically, the low pass filtering method of an input video signal according to the present invention is performed by multiplying a low pass filter by an input video signal. That is, when the value of each pixel for an image of one block of N × M (for example, 8 × 8 blocks) is f (x, y), one block is shown as shown in FIG. 2 as an example. In the case of an 8x8 block, the position values x and y of the pixels in the horizontal and vertical directions have integer values between 0 and 7, and each value has a level value between 0 and 255. That is, as can be seen from FIG. 2, the horizontal and vertical position values of each pixel of the 8x8 block have a value of f (0, 0) to f (7, 7).

On the other hand, as the low pass filter in the present invention, as shown in FIG. 3 as an example, the one-dimensional low pass filter coefficient is assumed to have seven orders. The low pass filter is an example of a low pass filter that band-limits the signal to have a frequency bandwidth of fs / 4 because its frequency bandwidth is fs / 2 when the sampling frequency of the input video signal is fs.

Accordingly, in the low pass filtering block 230, the low pass filtering of the video signal according to the present invention is performed by performing one-dimensional low pass filtering on each direction since the input video signal is a two-dimensional signal in the horizontal and vertical directions. Can be implemented. This process is illustrated in detail in FIG. 4, which shows the horizontal filtering at position (0, 4) and the vertical filtering at position (3, 0). That is, if the low pass filtered signal in the vertical direction at the position of (x, y) is z (x, y), it is calculated by the above and below equation (5).

In the above formula (5), T means the order, so T = 7.

Thus, u and v values have integer values between -3 and 3. In the above equation (5), the k (u) value is a filter coefficient value, and the f (x, y) value is a pixel value. In the above equation (5), if the value of f (x, yu) is less than 0, the value is 0. If the value is larger than the value of M-1 corresponding to the image size of one frame, the value of M-1 is M-1. Do it. This is a method of setting the end value when the pixel value exists only in the region, that is, 0 to M-1, and this filtering method is a technique known in the art.

Therefore, if filtering is performed at all pixel positions as in Equation (5) above, as shown in FIG. 4, for example, one-dimensional filtering in the horizontal and vertical directions can be obtained. In this case, according to the present invention, in the process of performing such filtering at all pixel positions, horizontal filtering may be performed first, and vertical filtering may be performed again or the order of the horizontal filtering may be performed again.

As a result, the low pass filtering block 230 applies one-dimensional low pass filtering to the input video signal when the filter control signal on the line L23 is high level, that is, when it is determined that the complexity of the image is large. By restricting, an image signal from which a high frequency component relatively insensitive to human visual characteristics is removed is generated on the line L11 and provided to the subtractor 110 and the current frame prediction block 180 of FIG.

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 that is relatively insensitive to human vision is removed through one-dimensional low pass filtering as described above. Therefore, the low frequency signal, which is a visually important component, can be encoded with less quantization error. If the passband of the frequency is not limited (high frequency component removal) 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. In the low frequency band), a lot of quantization errors are generated, and as a result, image quality deterioration due to quantization will be caused in the playback image of the receiver.

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 current 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, one-dimensional low pass filtering is applied to the human vision. By first removing high-frequency components of the insensitive image and then performing encoding such as MC-DCT and quantization, it is possible to effectively adjust the amount of bits generated after encoding 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, 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. Space and time complexity calculation means for calculating a final complexity value based on the complexity value and the motion average error value; A filter control for referring to the calculated final complexity value as the complexity of the frame to be currently encoded by the encoding means and for limiting the frequency passband bandwidth of the current frame input for encoding based on the calculated final complexity value Control means for generating a signal; And based on the generated filter control signal, provide the input current frame as the current frame signal for motion estimation and compensation without filtering to the encoding means or apply one-dimensional low pass filtering to the input current frame. And a one-dimensional low pass filtering means for providing a frame signal from which high frequency components are removed by limiting a pass band to the encoding means as a current frame signal for motion estimation and compensation. Coding system. The method of claim 1, wherein the filter control signal comprises: a first average error value for a plurality of frames transmitted per second obtained by averaging the calculated average spatial complexity value of each frame, the average spatial complexity value of each frame, A second average error value for a plurality of frames transmitted per second obtained by averaging a first standard deviation of the first average error value, the calculated motion average error value of each frame, and the motion average error value of each frame And a second standard deviation of the second mean error value. The image encoding system of claim 2, wherein the filter control signal is a logic signal of a high or low level. The image encoding system of claim 1, wherein the one-dimensional low pass filtering of the current frame is performed in units of 8 × 8 blocks. The method of claim 1 or 4, wherein the one-dimensional low pass filtering for the 8x8 block is performed sequentially in the horizontal-vertical or vertical-horizontal direction of each pixel position in the 8x8 block. An image encoding system having a bit generation amount adjustment function.