KR100203676B1 - Improved image coding system having functions for controlling generated amount of coded bit stream - Google Patents

Abstract

According to the present invention, in a coding system including MC-DCT and quantization, a complexity of an image to be currently encoded is calculated based on a quantization error value between a current frame input for encoding and a reconstructed previous frame, and the calculation result The present invention relates to a video encoding system having a bit generation amount adjustment function for adaptively adjusting a bit generation amount after encoding by adjusting a weight of a high frequency component during quantization according to the present invention. A quantization error value is calculated for each frame based on the pixel value differential signal between the previous frames, and the average quantization error value for a plurality of preset previous frames is calculated by averaging the calculated quantization error values of the corresponding frame. Calculation means; And refer to the calculated average quantization error values of the plurality of frames as the complexity of the frame to be currently encoded by the encoding means, and adaptively adjust the bandwidth in the low pass filtering of each DCT transform coefficient block generated through the discrete cosine transform. Weighting means for determining the weight corresponding to the calculated quantization average error value of the plurality of predetermined weights to provide to the encoding means, the encoding means, the encoding means is determined before quantization for each DCT transform coefficient block By restricting the pass frequency band of each DCT transform coefficient block based on the weight, and selectively filtering the high frequency component of each DCT transform coefficient block, it is possible to effectively control the amount of bits generated after encoding without increasing excessive step size in the quantization step. It can be.

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 a diagram illustrating a weight determination region determined based on the complexity of an 8x8 pixel block, for example, 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: quantization error calculation block 220: weighting 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 current frame and a reconstruction for encoding a video signal using a motion compensation differential pulse code modulation (MC-DPCM) technique. The present invention relates to an image encoding system having a bit generation amount adjustment function suitable for adaptively adjusting a generation bit amount after encoding with reference to a variation of an input image signal predicted based on a quantization error value between previous frames.

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 techniques with temporal and spatial compression techniques are known to be the most efficient, and these techniques are standardized by, for example, the World Standardization Organization. It is widely disclosed in the recommendations of MPEG-1, MPEG-2, etc. already made.

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 is described, for example, in Staffan Ericsson's “Fixed and Adaptive Predictors for Hybrid Predictive / Transform Coding”, IEEE Transactions on Communication, COM-33, NO.12 (December 1985, December), or “A motion by Ninomiy and Ohtsuka”. Compensated 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, which is a movement perpendicular to the image plane), while the motion vectors 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, 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, 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 transfer time here is related to the size (i.e. capacity) and transfer rate of the transfer buffer and is controlled so that no malfunction (data overflow or data underflow) occurs in the transfer buffer.

More specifically, the amount of bits generated for each frame at the time of encoding varies due to various factors (for example, the complexity of the image). In view of this, in the 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 above-mentioned problems of the prior art, in the coding system including MC-DCT, quantization, based on the quantization error value between the current frame and the reconstructed previous frame input for encoding Provides an image encoding system having a bit generation amount adjustment function that can adaptively adjust the bit generation amount after encoding by calculating the complexity of an image to be encoded and adjusting the weight of high frequency components during quantization based on the calculation result. Its purpose is to.

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 quantization error value is calculated for each frame based on a pixel value difference signal between the current frame and a reconstructed previous frame, and the quantization error values of the corresponding frames are averaged, respectively, to preset a plurality of previous frames. Average Quantization Error Value for Quantization error calculation means for calculating a; And referring to the calculated average quantization error value of the plurality of frames as the complexity of the frame to be currently encoded by the encoding means, and the bandwidth of the low pass filtering of each DCT transform coefficient block generated through the discrete cosine transform. A weight generating means for determining a weight corresponding to the calculated quantized mean error value among the plurality of predetermined weights that are adaptively limited and providing the weighted value to the encoding means, wherein the encoding means comprises: each DCT transform coefficient; By restricting the pass frequency band of each of the DCT transform coefficient blocks based on the determined weight before quantization of the blocks, by selectively filtering the high frequency component of each DCT transform coefficient block having a bit generation amount adjustment function 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. A block 150, an adder 160, a second frame memory 170, a current frame prediction block 180, a quantization error calculation block 210, and a weight generation block 220 are included.

Referring to FIG. 1, an input current frame signal is stored in a first frame memory 100 through a subtracter 110, a current frame prediction block 180, and a quantization error calculation block forming features in the present invention. Each provided at 210.

First, the subtractor 110 performs motion compensated predicted motion on the moving object provided from the current frame prediction block 180 described later through the line L19 from the current frame signal provided in the first frame memory 100 through the line L11. The current frame signal is subtracted, and as a result, the data, that is, the error signal representing the differential pixel value, is obtained by using discrete cosine transform (DCT) and one of quantization methods well known in the art through the image coding block 120. By encoding a series of quantized DCT transform coefficients. 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 L11 is provided to the quantization error calculation block 210 to be described later, the quantization error calculation block 210 is described later through the input image signal for the current frame on the line L11 and the line L16. The quantization error value is calculated by using the image signal of the reconstructed previous frame provided from the second frame memory 170, that is, it appears as a difference signal between the input image before encoding (DCT, quantization) and the previous image reconstructed after encoding. The quantization error value is calculated, and the calculated quantization error value is referred to as the complexity of the frame to be encoded. In the present invention, the human visual field is quantized in the image encoding block 120 based on the complexity calculated as described above. Adaptive (or selective) rejection (ie, filtering) of high frequency components that are relatively insensitive to characteristics.

Meanwhile, although the detailed illustration is omitted in FIG. 1, in the quantizer included in the image encoding block 120, a weight based on the calculated quantization error value information provided from the weight generation block 220 described later according to the present invention is used. In the quantization step, it is determined to remove (ie, filter) a high frequency component which is relatively insensitive to human visual characteristics. That is, the quantizer in the image coding block 120 adaptively (or selectively) limits the frequency bandwidth limited in the low pass filtering in the quantization step. 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 at the time of low pass filtering in the quantization step by using the weight set based on the calculated quantization error value information will be described in detail later.

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

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 such a 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. do.

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

On the other hand, in the current frame prediction block 180, the current frame signal on the line L11 provided from the first frame memory 100 described above and the reconstructed previous frame on the line L15 provided from the second frame memory 170 described above. A predetermined block (eg, 8 × 8 or 16 × 16) in a preset search range (eg, 16 × 16, or 32 × 32 search range) of a previous frame reconstructed using a block matching algorithm based on the signal. After predicting the current frame in units of DCT blocks, the predicted current frame signal is generated on the line L19 and provided to the adder 160 of the subtractor 110 described above. 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 above-described etropy coding block 130, the quantized DCT transform coefficients on the line L13 together with the sets of the motion vectors on the line L17 generate an encoded bit stream by encoding, for example, through a variable length encoding technique. do.

Meanwhile, the quantization error calculation block 210 of the present invention calculates the quantization error between the current frame on the line L11 input for encoding and the previous frame on the line L16 reconstructed and reconstructed after encoding, that is, input before encoding (DCT, quantization). A quantization error value is calculated for each frame that may appear as a difference signal between an image and a previous image reconstructed after encoding, and the calculated quantization error value is referred to as the complexity of the next frame to be encoded.

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. Each pixel value at position x, y) corresponds to Ip (x, y) and Ic (x, y), and the quantization error QE (Quantization Error) at this time is calculated by the following Equation (1).

Here, the quantization error QE value calculated using Equation (2) can be said to substantially reflect the complexity of the image data. Accordingly, the present invention uses the quantization error QE value calculated using the current frame and the previous frame reconstructed after reconstruction after encoding as the complexity of the next video signal input for encoding. In this case, the calculated quantization error QE value may be considered to be related to the information amount (bit generation amount) of the image. If the current encoded image is complex, the calculated quantization error QE value will be relatively large, and vice versa. The calculated quantization error QE value will be small.

In light of this point of view, the quantization error QE value between the current frame and the reconstructed previous 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 reconstructed previous frame signal required in the process of performing the motion estimation and compensation is stored in the frame memory, such an encoding system as described above. Using a part of, it is easy to implement the desired quantization error QE value which can be represented as the difference signal between the current frame and the reconstructed previous frame described above without any further calculation. Then, the quantization error QE value calculated in the quantization error calculation block 210 as described above is provided to the next weight generation block 220.

On the other hand, the weight generation block 220 is based on the quantization error QE value provided from the quantization error calculation block 210 based on the plurality of predetermined weights (e.g., The weights corresponding to four weights for setting different filter coefficients) are determined and generated on the line L23 and provided to the image coding block 120 described above. Here, the weights determined and provided to the image encoding block 120 divide the frequency values according to the frequency domain division value B calculated as follows immediately before quantization of the input image (ie, the DCT transform coefficient). In this case, the area division value B necessary for dividing the frequency domain is calculated by the following method, and a process of setting the frequency domain using the area division value B will be described later in detail with reference to FIG. will be.

In more detail, the weight generation block 220 outputs the region division value B using the quantization error QE value output from the quantization error calculation block 210 as shown in Equation (2) below.

The process of obtaining MQE and SQE in Equation (2) is as follows. That is, when the frame rate of the video signal is 30, the average of 30 QE values calculated for 1 second is MQE, and the standard deviation of the value is SQE. Therefore, the area separation value B can be obtained by comparing the MQE and SQE values thus obtained with the QE values generated in each frame. That is, in the weight generation block 220, the area division value B is determined using the average and the standard deviation of the QE values generated during the previous 30 frames. As a result, the currently generated QE value obtained through this process is used to calculate the average value and standard deviation of 30 frames. Accordingly, the weight generation block 220 discards the first obtained QE value (the oldest QE value in time) among the previously generated QE values of 30 frames and uses the newly input QE value from the quantization error calculation block 210. Find the mean and standard deviation. Of course, the currently generated QE value is not used to calculate the mean and standard deviation after the 31st frame.

As is apparent from Equation (2) above, the area discriminating value B has an integer value between 1 and 4, which is an error signal (the current frame on the line L11 and the prediction frame on the line L19) output from the subtractor 110 described above. This is to adaptively adjust the bandwidth according to the QE value in the two-dimensional low pass filtering before quantization of the DCT transform coefficients of the discrete cosine transform of the difference signal).

Next, a process of adjusting the weight using the area division value B calculated as described above will be described in detail with reference to FIG. 2 attached as an example.

Referring to FIG. 2, when the calculated QE value is greater than the MQE value and the B value calculated in the weight generation block 220 is 1, the image coding block 120 discretes an error signal input from the subtractor 110. Cosine-transformed DCT transform coefficients are directly quantized to a predetermined step size determined based on data fullness information of the transmission buffer 140 without weighting for frequency band limitation, that is, without two-dimensional low pass filtering. Will be performed. This means that the input image is not complicated and simple and is encoded (quantized) as it is without two-dimensional low pass filtering in the same manner as in the conventional method.

Unlike the above, as shown in FIG. 2, when the B value has a value of 2, 3, or 4, the image coding block 120 divides all frequencies below the dotted line corresponding to each value by 2. Perform quantization. That is, when the B value is 4, frequencies below the dotted line such as F (1, 7), F (2, 6), etc. in FIG. 2 are all divided by 2 to be encoded (quantized). Therefore, when the encoding (quantization) is performed through the above process according to the present invention, the low frequency signal, which is a visually important component, can be encoded while generating less quantization error. If the weight is not limited to limit the low pass bandwidth of the frequency as in the present invention despite the complicated image, all the frequency bands (high frequency to low frequency) are increased because the amount of bits generated after encoding increases and the quantization step size becomes large. Quantization error is generated a lot, resulting in deterioration of image quality due to quantization in the playback image of the receiving side.

Accordingly, in the weight generation block 220 of the present invention, a quantization error calculated every frame using the current frame on the line L11 input for encoding and the previous frame on the line L16 reconstructed and reconstructed after encoding for motion estimation and compensation. The value information is used to calculate the complexity of the input image, and based on the complexity calculation result, a weight is generated to limit the bandwidth of the high frequency component of the image, thereby performing low pass filtering (that is, two-dimensional low pass filtering) before quantization. By primarily reducing the bit amount of the image, even if it is a complex image, it is possible to sufficiently encode (quantize) low frequency components that are visually important information while suppressing an excessive increase in the step size in the quantization step. Therefore, it is possible to suppress the deterioration of the image quality due to the quantization error in the reproduced video reconstructed by the receiving side decoding system.

Next, the process of performing conventional DCT and quantization and the process of quantizing while weighting based on the complexity of the image according to the present invention will be described based on the results of experiments performed by the present inventor on an image signal of one block as an example. do.

In the following description, an example of reproduction of a video signal of a block composed of 8x8 pixels is performed through conventional DCT, quantization, inverse quantization, and IDCT processes. In this case, it is assumed that the DCT conversion coefficient is divided by the step size QP * 2, and the step size is 2 (QP = 1).

Table 1 below shows the video signal for one block (8x8 pixels). As can be seen from Table 1, it can be seen that each pixel has a level value between 0 and 255. , Each pixel value for the position in the vertical direction.

Table 2 below shows the result of performing 8 × 8 DCT in the horizontal and vertical directions on a block of the video signal having the pixel values as shown in Table 1 above. At this time, the value at each position of the result means a frequency component in each direction.

Table 3 below shows the result of performing quantization on a DCT transform coefficient block (8x8) having frequency components as shown in Table 2 above. The quantization step size in this process was calculated as QP * 2, taking the case where QP is 1, that is, the step size is 2. As a result, the value at each position corresponds to the result of dividing the value of Table 2 above by 2. Therefore, as a result of performing this operation (quantization), each of the quantized DCT transform coefficients is smaller in size than the respective DCT transform coefficients shown in Table 2, and the number having a value of 0 increases. Able to know. As a result, this means that the amount of information to be transmitted after encoding is reduced. If the QP value at this time is large, the resulting quantized DCT transform coefficients in Table 3 are not only smaller in size but also smaller in number with a value of 0, so that they are transmitted after encoding. The amount of data to be generated will be further reduced. As described above, this is a conventional method of controlling the amount of information to be transmitted by adjusting the QP value in the quantization step.

Meanwhile, the present invention does not adjust the amount of bits generated after encoding only by adjusting the conventional quantization step size as described above, but instead of reconstructing and reconstructing after encoding for the current frame and motion estimation and compensation inputted for encoding. The complexity of the image to be currently encoded is calculated using the quantization error value information calculated every frame using the frame, and based on the calculated complexity, the image currently input is a complex image (that is, a large number of bits are generated after encoding). Image), a plurality of weights having a predetermined high frequency cutoff level are adaptively given before the quantization step to limit the pass frequency band of the DCT transform coefficients.

That is, Table 4 shows, as an example, DCT conversion given a weight corresponding to the case where the area division value B calculated in the weight generation block 220 shown in FIG. 1 is 4 for one block (8x8 DCT block). Shows the result of quantizing the coefficients. The result of Table 4 shown above is a result of quantizing the frequency part below the dotted line by 2 in Table 4 as compared with the quantized DCT transform coefficient values of Table 3, and has a large number having a level of 0. Also, it can be seen that the magnitude of each quantized DCT transform coefficient value is also two times smaller. As a result, in the present invention, by applying a weight based on the complexity of the image determined based on the quantization error value information calculated every frame, the high frequency components of the input image are adaptively removed and then quantized, thereby causing excessive quantization. It is possible to effectively adjust the amount of bits generated after encoding without increasing the step size. In the following Table 4, the dotted line shown as determined according to the B value of 4 corresponds to the portion of the B value weighted 4 in FIG.

As described above, according to the present invention, the current frame to be encoded using the current frame input for encoding and the quantization error value information calculated for each frame using the previous frame reconstructed and reconstructed after encoding for motion estimation and compensation If the current input image is a complex image according to the calculation result, the complexity of the image is calculated, and a corresponding weight is first applied to remove the high frequency components of the image insensitive to the human vision, and then the DCT, quantization, etc. encoding is performed. By doing so, it is possible to effectively adjust the amount of bits generated after encoding without excessively increasing the 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. Quantization error is calculated for each frame based on the pixel value difference signal between the reconstructed previous frames, and the average quantization error values for the plurality of preset previous frames are calculated by averaging the calculated quantization error values of the corresponding frames. Error calculation means; And referring to the calculated average quantization error value of the plurality of frames as the complexity of the frame to be currently encoded by the encoding means, and the bandwidth of the low pass filtering of each DCT transform coefficient block generated through the discrete cosine transform. And a weight generating means for determining a weight corresponding to the calculated quantized mean error value among the plurality of preset weights that are adaptively limited, and providing the weighted value to the encoding means, wherein the encoding means comprises: each DCT transform coefficient; By restricting the pass frequency band of each of the DCT coefficient of change coefficient block based on the determined weight before quantization of the blocks, by selectively filtering the high frequency component of each DCT transform coefficient block having a bit generation amount control function Video encoding system. The method of claim 1, wherein each of the predetermined weights is an average error value for a plurality of frames transmitted per second obtained by averaging the calculated average quantization error value of each frame, the average quantization error value of each frame, and the average. An image encoding system having a bit generation amount adjustment function, which is determined using a standard deviation of an error value. The bit generation amount adjusting function of claim 1 or 2, wherein the predetermined plurality of weights includes four weights of integer values for setting different filter coefficients of the respective DCT transform coefficient blocks. Image coding system. The image encoding method according to claim 3, wherein the encoding means selectively filters high frequency components of the respective DCT transform coefficient blocks according to the determined weight through two-dimensional low pass filtering. system. The bit generation amount according to claim 4, wherein the encoding means performs the quantization by dividing the high frequency components below the determined weight by two to selectively filter the high frequency components of the respective DCT transform coefficient blocks. Image coding system with control function.