Method for decreasing distortion of compressed video image and device for realization of the method

FIELD: radio engineering, possible use for digital processing of video signals, transferring the image.

SUBSTANCE: in accordance to the invention, the image being processed is divided on blocks with following transformation of each block using discontinuous quantum transformation, result coefficients are quantized and encoded, supporting points are computed and linear interpolation is performed, while before the stage of supporting point selection, one of the supporting points on edge limit of block is selected and a supporting point on opposite limit block is calculated using additional low frequency filters, after that linear interpolation is performed between thus computed supporting points.

EFFECT: improved quality of compressed video image with insignificant CPU resource costs.

2 cl, 4 dwg

 

The invention relates to the field of radio and can be used for digital processing of video signals, transmitting the image.

Limited bandwidth telecommunications channels makes high-quality transmission of images, particularly dynamic. Therefore, when the transmission of video signals resort to their compression by removing duplicate or almost malaysiensis fragments and then restore them when playing a video.

Existing international standards on the view video (MPEG-1, MPEG-2, MPEG-4, H-261, H 263, and others) do not regulate methods of video compression, but only determines how should look a bit stream of the encoded video signal, therefore, specific algorithms are the intellectual property of the manufacturers of the equipment. For example, Siemens AG patented "a Method for decoding compressed video data with a reduced need for memory" (published application similar EN 97104164) [1]. Also known American design "Method nizkoshumnoe encoding and decoding" (Russian patent-similar EN 2201654) [2], in which the requirements for bandwidth digital decoder is reduced due to the fact that the standard G2-encoder is preceded by the adaptive processor of the digital image that hell is ptive filters the signal at low frequencies. Japanese experts suggested correction pattern image quality", is described in Russian patent application-analog, EN 2000133250 [3]that implements the schema that contains the count of the frequency of appearance of the brightness level, a linear interpolator and the offset image quality. Well-known domestic development of methods of digital processing of signals, in which the compression of video signals, transmitting dynamic image. In the patent RU 2131172 described "Interpolation method for compressing a television signal" [4], the essence of which consists in the artificial exclusion of signal lines and restore using interpolation in fragments is not excluded parts of the lines, thereby reducing the redundancy of a television signal.

It is known that during the development of international standards has determined the General principles of video processing and compression (see ISO-11172 and ISO-13818, parts 1, 2, 3, November 1994) [5]. In particular, it was found that the process of compressing the digital video signal may be divided into several steps: converting the analog signal into digital form, pre-processing, discrete cosine transformation, quantization, and encoding.

The main idea of the MPEG standard is that of the entire stream of video image completely peereducators selected reference frames, for the others transmitted only their changes in relation to the reference.

Actually in a moving image from frame to frame in most cases, changing only part of it. For example, when the speech of the speaker in the news only changes his body language. Full frame change when the next frame cannot be recovered as the previous change (in this case, it is easier to transmit the frame itself), is relatively rare. For example, in American films, it's usually 4-5 seconds in European (especially Russian), this interval is much greater.

For this reason, MPEG-2 defines three types of frames:

I-frames (intra-frames, P-frames (predicted frames);-frames (bidirectional frames).

I-frames are full still image and in addition are used to build the P - and b-frames. P-frames, that is "predictable", and are based on the latest (from the point of view of the receiver) of the received I - or P-frame. However if it is much different (for example, there was a change of plan), the P-frame is encoded as an I-frame.

The most difficult restored In frames or "prediction". This frame can be built either as a continuation of the previous I(R)-frame, or as a precursor followed by I(R)-frame, or as an interpolation between the two. If the frame is significantly different from the first and from the second, it is encoded as an I-frame.

<> All types of frames are grouped together in a specific sequence. A group of 12 frames forms a so-called GOP (Group of Pictures). Thus, at 25 frames per second, a new I-frame comes after a maximum of 12×(1/25)=0,48 seconds. With him is restored to full (in a sense) the identity of the transmitted and received image). Due to the fact that when decoding to obtain In-frame, you must have the following P-frame, the transmission frame sequence should be strictly defined.

To encode individual frames there is also a special procedure. For example, to encode the color image is in YUV used in conventional television. The image is decomposed not on the three color channels (RGB)and two color channels (U, V) and luminance channel (Y).

The image in the luminance channel is essentially a black and white image. Noticed that one of the peculiarities of perception of the image by the human eye is that it has a higher resolution for the luminance channel (Y)than in the color channels (U, V). So rasleela color frame on these three components, we can expose the layers U and V greater compression than the layer Y. This principle was used when creating the analogue colour television, where U, V are transferred to the E. at the same time, and turn.

An I-frame is encoded as a static image in the following way. Each layer of the frame is divided into blocks of size 8×8 points and runs in the danger discrete cosine transformation (DCT, DCT). DCT is fully reversible transformation. In fact DCT is a special case of the Fourier transform for the even functions when the function is decomposed only on the cosine harmonics.

When using DCT instead of the pixel value (i.e. level of chroma and brightness) in the cell block is DCT coefficient. That is, the block is converted to its two-dimensional spectrum. Typically, the energy spectrum of the image is concentrated in the low-frequency harmonics, so the odds are closer to the upper left corner, have higher values than others. The smaller neighboring pixels differ from each other in the source block, the closer to zero the values of most of the DCT coefficients.

For pixels monotonous image, the DCT coefficients are zero, except for the coefficient in the upper left corner, which specifies the intensity of the image.

The resulting coefficients quanthouse (i.e. rounded to some extent 2). The main task is to increase the number of zero coefficients. Essentially discarded high-frequency harmonics. As experience shows, it is usually almost the Eski does not affect the quality of the image.

The resulting set of binary vectors (factors) shrink-known Huffman code. Thus is formed a compressed I-frame, which with the known loss of quality can be restored independently of other frames. P - and b-frames are encoded based on their differences from the reference I - and P-frames. Therefore, they are stronger in compression than I - frames. When encoding P-frames (B-frames are coded almost the same way), he also is divided into 8x8 blocks and compared with the original frame (we assume that this is an I-frame, although it may be the previous P-frame). If some block in the encoded P-frame coincides with the similar block in the reference frame, it is sufficient to indicate that he is the same. Another case is finding exactly the same block in the reference I-frame, but in another position, so instead of block P-frame, you can specify only a reference to another block I-frame in the form of the displacement vector. The remaining blocks are encoded as in the case of I-frame.

Note that if in a moving image portion of the object will move progressively (and it happens often), multiple blocks will be encoded the same displacement vector. Subsequent compression of the Huffman-this will give an additional increase in the degree of compression of the P-frame. On the other hand, when increasing the degree of compression is a big part of the coefficients becomes Rav is Oh zero in the result of quantization and, thus, the block is coded or only an average brightness value or a small amount of low-frequency DCT coefficients. Because the blocks do not overlap, due to the difference of the average luminance values of the neighboring blocks between them there is a prominent difference in brightness, and brightness fluctuations, as a rule, in General, form a regular cellular structure on the decoded image. If the video compression used motion compensation, block structure may change over time, losing the regularity of the arrangement of the blocks, due to the fact that the blocks of the reference frame can be moved in the predicted frames.

Each of the above development [1-5] can be solved only some aspects of the complex problem of "record/playback video quality", with even a slight improvement of the quality of the compressed video requires a significant computational cost, which prevents widespread implementation of these developments. Particularly difficult is the elimination of distortion of the dynamic image in the form of a cellular structure.

It seems that the combination of low-pass filtering with linear or bilinear interpolation can provide a significant improvement in the compressed dynamic video at modest calculate what lnyh cost. The advantage of linear interpolation is the simplicity of the calculation, namely, it can be calculated using the half-sum operation or use pre-calculated tables. The lack of linear interpolation is that it is less effectively removes block distortion compared to the low-pass filter, or by comparison with interpolation polynomials of higher orders.

Closest to the proposed method is described in thesis Joceli Mayer, "Blending Models for Image Enhancement and Coding", Ph.D. Thesis, University of California, Santa Cruz, Advisor: Prof. Glen G. Langdon, Ph.D., December 1999 [6]; in which is provided the use of interpolation of second order (in particular by Bezier polynomials). However, the use of the interpolating polynomial over the first order requires a much higher computational cost than linear interpolation (i.e. the interpolation polynomial of the first order).

The present invention solves the problem of improving the quality of compressed video at negligible computational cost.

To achieve the mentioned technical result in the proposed method, including breaking the image into blocks and then converting each block using DCT, quantization and encoding the resulting coefficients provide you Islena corrective characteristic points, usually called anchor points, using low-pass filtering and then carry out a linear interpolation between calculated and reference points. This procedure is somewhat similar to the choice of the reference frame in the MPEG standard. In this case, since the number of anchor points interpolation is less than the number of processed pixels in the image, the total computational complexity caused by the introduction of low-pass filtering, increases slightly.

Distinctive features of the proposed method is the procedure of choice of reference points by using low-pass filtering and replacement processing pixels of an image processing significantly smaller number calculated reference points. Thus vacated computing power are directed to the implementation of one of the types of interpolation - rows, columns, or even bilinear interpolation requires the most computational costs.

The proposed method is illustrated by drawings on which is shown:

Figure 1 - scheme of partitioning the image into blocks and processing order of blocks.

Figure 2 - scheme of using linear interpolation for string processing block of the image.

Figure 3 - use bilinear interpolation for block processing of the image.

Figure 4 - functional sh is mA device implements the present invention.

Figure 1 shows the partitioning scheme of the processed image 1 on the processed blocks 3 and sequence 4 processing units 3. When processing the whole image 1 is divided into square blocks 3 with a size smaller than the block size 2 DCT, used for encoding. The most profitable to use the block 3 with the linear dimensions of the two times less unit 2 DCT (i.e. a block of 4×4 if the block size is 2 DCT 8×8). The blocks 3 are treated consistently and the result replaces the processed block 3. Although the sequence of the 4 treatment no matter wherever it is further assumed sequence 4 processing units 3 left-to-right-top-to-bottom.

Figure 2 shows the scheme of using linear interpolation to reduce block distortion on the line of the processed block 3. This assumes that the block distortion on the columns are absent or insignificant. Left anchor point 7 choose the rightmost border of the left (possibly processed in the previous step) unit 3. Right anchor point 7 is calculated using a low-pass filtering the pixels that lie on both sides of the border block 3. From the point of view of efficiency calculations as a low-pass filter it is advisable to use a filter of length 2 and weights {0.5; 0.5}, or in other words, count Paulus the MMU brightness of the pixels on both sides of the border block 3. After computing the left and right anchor points 7 values of pixels between them, replace the values calculated by the linear interpolation formula:

Pixel[i]=L+(P-L)/n·i i=[1,n];

Where L is the left anchor points, P - value of the right anchor points, n is the number of prediction points, i is the number of predicted picture element.

This procedure is performed for each row of the processed block 3.

Reducing block distortion on the columns of the image are produced similarly, it is assumed that the distortion of the lines are absent or insignificant.

Figure 3 shows a scheme using bilinear interpolator 8 to reduce block distortion simultaneously in rows and columns of the block image.

In the case of bilinear interpolation to calculate values for the four reference points. It also used low-pass filters 5, connected to the inputs of the bilinear interpolator 8. It is preferable to use low-pass filters 5 with the following factors:

0
FilterThe coefficient for the top left pointThe ratio of the upper right pointThe ratio of the lower left pointThe coefficient of the bottom right point
Low-pass filter 510.50.50
Low-pass filter 520.500.250.25
Low-pass filter 530.50.2500.25
Low-pass filter 540.50.250.250

With this choice of coefficients point processed in the previous step, have great weight.

Figure 4 shows a functional diagram of the device that implements the proposed method.

The device consists of a classifier 10 blocks, analyzing blocks 3 and detecting the presence and type of block distortion, and a set of low-pass filters 5 and interpolators 11-13 engaged in the processing of received data. In the absence of block distortion in the processed block 3, the interpolation is not applied. In the presence of block distortion unit 3 process depending on the type of block distortion in one of three interpolators: linear interpolator 11 rows, linear interpolator 12 columns, or bilinear interpolator 13. The choice of the type of the classifier 10 blocks is carried out, for example, based on the following requirements:

- the presence of block distortion by rows (columns) is fixed if all rows (columns) of the processed block 3 with the holding not more than one of the brightness difference and the absolute value of the difference does not exceed the doubled step quantization of DCT coefficients for the block 3.

In fact, data from the processed block 3, located in the buffer 9 image, is fed to the input of the classifier 10 and the switch 14, reacting as the switch 15, in command of the classifier 10. In the absence of block distortion switches 14 and 15 are installed in the upper position (see figure 4), while the signals from the processed block 3 pass the filter unit 5 and unit interpolators 11-13. This means that the correction block distortion in this case is not required. If the classifier 10 detects the presence of block distortion, depending on the identified type of distortion, the switches 14 and 15 activate the appropriate line: interpolation of rows, columns, or bilinear interpolation.

1. A method of reducing distortion of the compressed video image including breaking the image into blocks and then converting each block using DCT, quantization and coding of the resulting coefficients, the calculation of reference points and implementation of linear interpolation, wherein before the step of calculating the control points shall select one of the control points at the border of the block and an anchor point on the opposite boundary of the block is calculated using an additional low-pass filters, followed by a linear interpolation between the calc is lennemi thus anchor points.

2. Device for reducing the distortion of the compressed video image consisting of classifier blocks, identifying the type of block distortion in the processed block, and three interpolators, one of which carries out a linear interpolation line, the second performs a linear interpolation on the columns, and the third performs bilinear interpolation in rows and columns, characterized in that the inlet of each interpolator has a low pass filter and classifier performs the selection and inclusion of specific interpolator depending on the identified type of block distortion.



 

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