KR100203659B1 - 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 two-dimensional discrete cosine transform according to the result of the calculation, 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. 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 etropy encoding, and is preset to limit the frequency passband of the current frame input for encoding. Control means for generating a bandwidth determination signal corresponding to the calculated final complexity value among the plurality of bandwidth determination signals; Discrete cosine transform means for converting an image signal in a spatial domain with respect to an input current frame signal into two-dimensional DCT transform coefficients in a frequency domain in M × N block units by using a cosine function; Quantization means for quantizing two-dimensional DCT transform coefficient blocks in M × N units using a quantization parameter value to a finite number of values; Frequency selecting means for determining a high frequency pass band for the quantized DCT transform coefficient blocks based on the generated bandwidth determination signal, and limiting the high frequency pass band of each quantized DCT transform coefficient block to the determined bandwidth; And restoring the original signal before encoding by performing inverse quantization and inverse discrete cosine transform on each of the bandwidth-limited quantized DCT blocks, and the current frame signal for motion estimation and compensation of the bandwidth-limited frame signal restored to the original signal. By including the image reconstruction means provided to the encoding means, it is possible to effectively adjust the amount of bits generated after encoding without excessively increasing the step size during quantization in the encoding means.

Description

IMPROVED IMAGE CODING SYSTEM HAVING FUNCTIONS FOR CONTROLLING GENERATED AMOUNT OF CODED BIT STREEM

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 detailed block diagram of the frequency selection block of FIG.

3 shows a crystal 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: four, space complexity calculation block 220: control block

230: frequency selection block 2310: DCT block

2320 quantization block 2330: frequency selector

2340 inverse quantization block 2350 IDCT block

The present invention relates to a video encoding system for compression encoding a video signal, and more particularly, to predicting a video signal based on a motion compensation error value when compressing the video signal using a motion compensation pulse code modulation (MC-DPCM) technique. The amount of bits generated after encoding is adapted by referring to the variation of the input video signal predicted based on the time complexity between the previous video and the input video 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-definition 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 schemes that push data, the hybrid coding scheme combining the stochastic coding technique with the temporal and spatial compression technique is known to be the most efficient, and these techniques have already been proposed by the World Organization for Standardization. It is widely disclosed in the recommendations of enacted 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 the current frame according to the motion of the object to generate a differential signal representing the difference between the current frame and the prediction time. This can be done for example by Staffan Ericsson's Fixed and Adaptive Predictors for Hybrid Predictive / Transform Coding, IEEE Transactions on Communication, COM-33, NO, 12 (December 1985), or A motion Compensated Interframe Coding Scheme by Ninomiy and Ohtsuka. for Television Pictures, IEEE Transactions on Communication, COM-30, NO.1 (1982, January).

In general, two-dimensional DCT converts a digital image data block, for example, an 8x8 block, into a DCT conversion coefficient by using or removing 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, a zigzag scan, a VLC, or the like 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 a 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-by-block motion estimation method using a block matching algorithm, and the other is a pixel-by-pixel 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 determined for each pixel. Since a large amount of motion vectors occurs, it is impossible to transmit substantially all of the motion vectors to the receiver.

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, an interframe displacement vector (the degree to which the block has moved between frames) for the entire block is added to the current frame being 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 here is related to the size (i.e. capacity) of the transmission buffer and the transmission, and is controlled so that no malfunction (data overflow or data underflow) occurs in the transmission buffer.

More specifically, various factors (e.g., inverse complexity) may cause a different amount of bits generated for each frame during 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 status 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 increasing the step size, and vice versa, by adjusting the quantization step size to be smaller 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 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 severe 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 the MC-DCT, quantization, the time complexity and motion between the previous image and the input image predicted based on the motion compensation error value The complexity of the image to be currently encoded is calculated based on the spatial complexity of the previous frame reconstructed for estimation and compensation, and the high frequency component of the input video signal is selectively removed using the two-dimensional discrete cosine transform according to the calculation result. Accordingly, an object of the present invention is to provide a video encoding system having a bit generation amount adjustment function capable of adaptively adjusting a bit generation amount after encoding.

In order to achieve the above object, the present invention provides a discrete cosine transform, a 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 the step size based on the full state information of the bit stream stored in an output buffer. In the image encoding system, a spatial complexity value is calculated for each of the previous frames reconstructed for the motion estimation and compensation, and then averaged spatial complexity values of the respective previous frames are calculated for a plurality of preset previous frames. Calculate the mean spatial complexity value A motion compensation error value is calculated by adding each pixel value to each of the macroblocks with respect to the error signal, and then averaging the motion compensation error values of the respective macroblocks, respectively. 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; The plurality of preset bandwidth determination signals refer to the calculated final complexity value as the complexity of the frame to be currently encoded by the encoding means, and to adaptively limit 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 of the control unit; Discrete cosine transform means for converting an image signal in a spatial domain with respect to the input current frame signal into a two-dimensional DCT transform coefficients in a frequency domain in units of M × N blocks using a cosine function; Quantization means for quantizing the two-dimensional DCT transform coefficient blocks in M × N units using a quantization parameter value to a finite number of values; Frequency selecting means for determining a high frequency pass band for the quantized DCT transform coefficient blocks based on the generated bandwidth determination signal, and limiting a high frequency pass band of each of the quantized DCT transform coefficient blocks to the determined bandwidth; And performing inverse quantization and inverse discrete cosine change on each of the bandwidth-limited quantized DCT blocks to restore the original signal before encoding, and restore the bandwidth-limited frame signal restored to the original signal for the motion estimation and compensation. It provides a video encoding system having a bit generation amount adjustment function, characterized in that it further comprises a picture restoring means provided to the encoding means as a current frame signal.

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, time, spatial complexity calculation block 210, control block 220, and frequency selection block 230; Include.

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 moves from the control block 220 described later. The frequency of the input frame signal is determined according to a control signal (bandwidth determination signal for frequency domain classification) calculated based on the spatial complexity of the previous frame reconstructed for estimation and compensation and the time complexity of the image according to the motion compensation error value. The adaptive limitation, i.e., two-dimensional DCT, is used to remove high frequency components (which are insensitive to comparative human vision) of the input image. Reference will be made in detail later. 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 the 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 punctuation 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 quantization methods well known in the art via image coding block 120. Encode 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, 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 motion compensation error value calculated as described above. Based on the final complexity, which is calculated as a whole, two-dimensional DCT is used to restrict (or selectively) high frequency components that are relatively insensitive to human visual characteristics before the quantization step in the image coding block 120. 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, for example, through a variable length coding scheme, and are provided to the transmission buffer 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 reconstruction 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. Therefore, 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 reconstructed into the current frame prediction block 180 described later for motion estimation and compensation. do.

In addition, the restored and reconstructed previous frame signal stored in the second frame memory 170 is provided to the spatial complexity calculation block 210 to be described later through the line L16 to calculate 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 is not removed and the above-described second frame is removed. Based on the reconstructed previous frame signal on the line L15 provided from the frame memory 170, a predetermined range in the previous frame preset search range (for example, 16x16 or 32x32 search range) reconstructed using the block matching algorithm The current frame is predicted in units of blocks (for example, 8x8 or 16x16 DCT blocks), and the current frame signal predicted in the line L19 is generated and provided to the subtractor 110 and the adder 160, respectively. . 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 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 selected block (8x8 or 16x16 blocks) on the line L17 and provides the above-described entropy coding block 130. Here, the sets of detected motion vectors are predicted in the block of the current frame (8x8 or 16x16 block) and the preset search area (eg, 16x16 or 32x32 search range) in the previous frame. It is the displacement between the most similar blocks. Thus, in the entropy coding block 130 described above, the quantized DCT transform coefficients on the line L13 together with the sets of the traction vectors on the line L17 generate an encoded bit stream by encoding, for example, through a variable length encoding technique. do.

Meanwhile, in the space-time complexity calculation block 210 according to the present invention, the average error AE (time error) of the time complexity of the input image based on the error signal between the current frame and the predicted frame obtained through the motion compensation between the current frame and the restored previous frame. Calculate the Average Error, calculate the spatial complexity ACT (Activity) of the previous frame for motion estimation and compensation, and then adaptively remove high frequency components before the quantization step using these two complexity values (AE, ACT). Complexity C (Complexity) is generated to generate a weight determined to be determined. In this way, when the complexity of an input image is calculated by referring to both the complexity of the time domain and the complexity of the spatial domain according to the present invention, the complexity of only one region is calculated. Compared to refer only to (complexity of time domain or spatial domain), the calculation amount may be slightly higher, but more accurate It is possible to calculate the complexity of the input image clearly.

First, in the time and space complexity calculation block 210, for calculating the space complexity value ACT, the space complexity of the image for the reconstructed previous frame provided from the second frame memory 170 through the line L16 is calculated, that is, the image. 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. In this case, based on the spatial complexity of the previous frame and the temporal complexity described below, HF adaptively removes (ie, filters) high frequency components insensitive to human visual characteristics from the input image.

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, so that the distribution of transformed coefficients 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 that should be substantially encoded and transmitted.

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

In Equation (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 current frame to be encoded is that the characteristics of the video signal do not change rapidly every frame.

Therefore, 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 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, the encoding system stores the reconstructed previous frame signal required in the process of performing motion estimation and compensation in the frame memory. Using part of the system, one can easily obtain the spatial complexity ACT value of the previous frame recovered.

On the other hand, in order to calculate the time complexity, the time and spatial complexity calculation block 10 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 16 × 16 units (i.e., macroblocks), in which case the size of the search block in the previous frame is 32 × 32 units from the previous frame reconstructed using the motion vectors detected by the motion vector. 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 can be calculated.

For example, when the reconstructed image of the previous frame is called Ip and 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), the motion vector for the i, j th macroblock is MVX (i, j) MVY ( i, j), the motion compensation error value E (i, j) for the i, j-th macroblock is calculated by the following equation (3), wherein the motion vector is the current frame prediction block 180 described above. This is the value already detected in the motion estimation process at.

In the above Equation (3), L denotes the horizontal and vertical size of the macro block. If the image signal of one frame has M × N size and one macro block has L × L size, this image The number of macro blocks for the signal will be (M / L) × (N / L). For example, assuming 16 × 16 macroblocks for a 352 × 288 input video signal, 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, using the equation (4) below, the entire frame is The average error AE value for the image is calculated, and the calculated value is obtained by averaging the motion compensation error value with respect to the entire image of one frame average error AE.

In Equation (4), P and Q correspond to the number of macro blocks 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 obtained by averaging the motion compensation error values of all such macroblocks 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 aspect, 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 the macro block described above without further calculation.

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

Meanwhile, the control block 220 generates a frequency bandwidth determination signal B on the line L23 for limiting the frequency of the input image based on the final complexity C value provided from the temporal and spatial calculation block 210. Provided at 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.

More specifically, the process of outputting the bandwidth determination signal B for frequency domain classification using the lowest complexity C value output from the time and space complexity calculation block 210 in the control block 220 is expressed by Equation 5 below. .

The process of obtaining MC and SC in Equation (5) is as follows. That is, when the frame rate of the video signal is 30, the calculation degree for one second is 30 C values, the mean value is MC, and the standard deviation 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 current C value obtained through this process is again used to calculate 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 time and space 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 differential cosine transformed discrete cosine transform.

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, two-dimensional frequency conversion process uses two-dimensional DCT, and in the frequency selection process based on the bandwidth determination signal B provided from the control block 220 described above. The passband is determined by the 2D DCT transformed video signal.

Next, in the frequency selection block 230 as described above, the input image is converted into two-dimensional DCT, and the frequency of the two-dimensional DCT converted video 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 performs two-dimensional DCT conversion on the input image. As is well known in the art, the DCT conversion process is well known to reflect the spatial similarity of the image signal. The converter method is widely applied in the process of encoding a video signal. Therefore, detailed description is omitted here. Therefore, the present invention uses DCT transform having such characteristics (reflecting spatial similarity) as an effective frequency selection technique according to the complexity of the video signal. The frequency selection process according to the present invention returns to the frequency domain reflecting the characteristics of the image signal better than the simple frequency converter method, and as a result, it is possible to increase the efficiency when selecting a frequency for the input image. That is, the same number of DCT transform coefficients has more information about the video signal than the Discrete Fourier Transform (DFT) transform coefficients.

FIG. 2 shows a detailed block diagram of the frequency selection block 230 according to the present invention shown in FIG. As shown in the figure, the frequency selection block 230 includes a DCT block 2310, a quantization block 2320, a frequency selector 2330, an inverse quantization block 2340, and an IDCT block 2350 of the present invention. .

In FIG. 2, the DCT block 2310 utilizes the similarity of the spatial domain of the video signal, and according to Equation (6) below, M × The two-dimensional DCT transform coefficients in the frequency domain of N units, for example, 8 × 8 units are converted to the next quantization block 2320.

In Equation (6), F (u, v) denotes a transformed DCT coefficient, and f (x, y) denotes an input video signal. Here, x, y means the horizontal and vertical position of the pixel data, u, v means the horizontal and vertical frequency in the transformed DCT coefficients. The quantization block 2320 then quantizes the DCT coefficients that have been two-dimensionally transformed through Equation (6) to a finite number of values through, for example, nonlinear operations. In this case, the QP value is used in the quantization process of the DCT transform coefficient. When the transformed DCT coefficient is F (u, v), F (u, v) /

An operation that takes an integer value by performing (2 * QP) is an example of a typical quantization process.

Meanwhile, in the frequency selector 2330, the bandwidth selection signal B for frequency domain division provided from the control block 220 of FIG. 1 through the line L23 is applied to the DCT conversion coefficients that have been sunny through the above-described process. Based on that, determine the frequency passed. As described above, the bandwidth setting signal B for frequency domain classification is an integer value between 1 and 4, and the selected frequency is as follows.

That is, the frequency selector 2330 selects a specific frequency from the converted frequency F (k, l). Here, k, l is an integer value between 0 and N-1. Therefore, the value output from the frequency selector 2330 becomes a signal from which a specific frequency component (ie, a high frequency component) is removed. For example, if N = 8, its pass frequency will be determined as shown in FIG.

As shown in FIG. 3, when the bandwidth determination signal B value for the frequency domain division provided from the control block 220 of FIG. 1 to the frequency selector 2330 of FIG. 2 through the line L23 is 1, the conversion frequency F ( k, l) are all selected, and when the B value is 2, 3, or 4, as shown in FIG. That is, when the value of B in FIG. 3 is 4, all frequencies below the dotted line such as F (1,7), F (2,6), etc. are mapped to 0.

Next, as described above, the quantized DCT transform coefficients of 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 previous image encoded are dequantized in the next block 2340. And the original signal (pixel data) through the IDCT block 2350. At this time, the inverse transformation process of the inverse quantized DCT transformation coefficient in the IDCT block 2350 is as shown in Equation (7) below.

In Equation (7), f (x, y) means inversely transformed image signal (pixel data), and F (u, v) means transformed DCT coefficient. Here, u and v denote horizontal and vertical frequencies in the converted DCT coefficients, and x and y denote horizontal and vertical positions of the pixel data.

As a result, in the IDCT block 2350, the subtractor 110 of FIG. 1 and the current frame prediction block 180 are removed based on the complexity of the image signal according to the complexity of the image, that is, the complexity of the image. According to the calculated bandwidth determination signal B, an image 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 (quantized) while generating less quantization error. If the low pass bandwidth of the frequency is not limited as in the present invention even though the image is complicated, 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 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, Calculate the final complexity of the current input image to be coded using the time complexity information between the input images, and when the current input image is a complex image based on the final complexity calculation result, give a corresponding weight to the human 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 adjusting 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, the motion estimation and compensation. Computing a spatial complexity value for each of the previous frames reconstructed for the purpose 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 signal Each macro block for 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-time complexity calculation means for calculating a final complexity value based on the complementarity value and the motion average error value; Among the preset plurality of bandwidth determination signals for adaptively limiting the frequency passband bandwidth of the frame input for encoding, by referring to the calculated final complexity value as the complexity of the frame to be currently encoded by the encoding means. Control means for generating a bandwidth determination signal corresponding to the calculated final complexity value; Discrete cosine transform means for converting an image signal in a spatial domain with respect to the input frame signal into a two-dimensional DCT transform coefficients in a frequency domain in units of M × N blocks using a cosine function; Quantization means for quantizing the two-dimensional DCT transform coefficient blocks in M × N units using a quantization parameter value to a finite number of values; Frequency selecting means for determining a high frequency pass band for the quantized DCT transform coefficient blocks based on the generated bandwidth determination signal, and limiting a high frequency pass band of the quantized DCT transform coefficient block to the determined bandwidth; And performing inverse quantization and inverse discrete cosine transform on each of the bandwidth-limited quantized DCT blocks to restore the original signal before encoding, and restore the bandwidth-limited frame signal restored to the original signal for the motion estimation and compensation. And a picture restoring means provided to said encoding means as a current frame signal. The method of claim 1, wherein each of the predetermined large width determination signals comprises: a first signal for a plurality of frames transmitted per second obtained by averaging the average spatial complexity value 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 difference 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 an average error value and a second standard deviation of the second average error value. The method of claim 1 or 2, wherein the plurality of predetermined bandwidth determination signals comprise four bandwidth determination signals of integer values for setting different filter coefficients of the respective DCT transform coefficient blocks. Image coding system with bit rate control. The image having a bit generation amount adjusting function of claim 1, wherein the discrete cosine transforming means converts an image signal in a spatial domain with respect to the current frame signal into two-dimensional DCT transform coefficients in units of 8x8 blocks. Coding system. The bit selector of claim 1 or 4, wherein the frequency selecting means maps a high frequency component below the generated bandwidth determination signal to a zero value for each of the quantized DCT transform coefficient blocks. Image coding system with generation amount control function.