KR102534377B1 - Method for quantization based on fast rate-distortion optimization, and apparatus for the same - Google Patents

Abstract

The present invention relates to a quantization method based on fast rate-distortion optimization performed by a quantization apparatus based on fast rate-distortion optimization. According to the present invention, initializing the context information by initializing the value of the first context information in the conversion block to 1 and initializing the value of the second context information in the conversion block to 0; scanning transform coefficients in the transform block and quantizing the transform coefficients; calculating quantized level values of the transform coefficients, and determining optimal quantized level values for positions of some transform coefficients within the transform block or subblocks within the transform block; and determining an optimal last significant coefficient for the subblock or the location of the partial transform coefficients, further comprising applying scalar quantization using a dead zone to the location of the partial transform coefficients or the subblock. A quantization method based on fast rate-distortion optimization is provided, including. According to the present invention, the complexity of quantization can be reduced.

Description

Quantization method and apparatus based on fast rate-distortion optimization

The present invention relates to a method and apparatus for quantization based on high-speed rate-distortion optimization.

Recently, as a broadcasting service having a high definition (HD) resolution is expanded not only domestically but also globally, many users are getting used to high-definition and high-definition images.

Accordingly, many institutions are accelerating the development of next-generation imaging devices. In addition, as interest in UHD (Ultra High Definition) having a resolution four times higher than that of HDTV increases along with HDTV, a compression technique for a higher resolution and higher quality image is required.

In addition, for image compression, an inter prediction technique for predicting pixel values included in the current picture from temporally previous or subsequent pictures, predicting pixel values included in the current picture using pixel information in the current picture Intra prediction techniques may be used. In addition, for image compression, an entropy coding technique may be used in which short codes are assigned to symbols with a high frequency of occurrence and long codes are assigned to symbols with a low frequency of occurrence.

However, the existing technology has a problem in that the complexity of quantization is high. Accordingly, a technique capable of reducing the complexity of quantization is proposed.

The present invention aims to solve all of the above problems.

Another object of the present invention is to provide reducing the complexity of quantization.

Another object of the present invention is to reduce the complexity of rate-distortion optimization based quantization.

In order to achieve the object of the present invention as described above and realize the characteristic effects of the present invention described later, the characteristic configuration of the present invention is as follows.

In the quantization method based on fast rate-distortion optimization performed by the quantization apparatus based on fast rate-distortion optimization according to an embodiment, the quantization method based on fast rate-distortion optimization performed by the quantization apparatus based on fast rate-distortion optimization includes transform initializing context information by initializing a value of first context information within a block to 1 and initializing a value of second context information within the conversion block to 0; scanning transform coefficients in the transform block and quantizing the transform coefficients; calculating quantized level values of the transform coefficients, and determining optimal quantized level values for positions of some transform coefficients within the transform block or subblocks within the transform block; and determining an optimal last significant coefficient for the subblock or the location of the partial transform coefficients, further comprising applying scalar quantization using a dead zone to the location of the partial transform coefficients or the subblock. can include

In the quantization method based on fast rate-distortion optimization performed by a quantization apparatus based on fast rate-distortion optimization according to another embodiment, the quantizing step scans transform coefficients in the transform block in a reverse diagonal order and quantizing the transform coefficient using a quantization offset value of 0.5.

In the fast rate-distortion optimization based quantization method performed by the fast rate-distortion optimization based quantization apparatus according to another embodiment, the step of applying scalar quantization includes a rounding operator, a sign, a quantization step size and The transform coefficient may be mapped to a discrete quantized level using a quantization offset, the dead zone is an interval in which an input value is output as 0, and the quantization offset may include adjusting the interval of the dead zone. .

In the fast rate-distortion optimization based quantization method performed by the fast rate-distortion optimization based quantization apparatus according to another embodiment, the step of determining the optimal quantized level value includes the last significant coefficient in the transform block fixing; and calculating rate-distortion costs for the remaining non-zero transform coefficients.

In the quantization method based on fast rate-distortion optimization performed by a quantization apparatus based on fast rate-distortion optimization according to another embodiment, the determining of the optimal last significant coefficient may include initial rate-distortion for the transform block Initializing with a cost value; and scanning in an inverse diagonal order using the quantized level values in the transform block and calculating a rate-distortion cost.

In the quantization apparatus based on fast rate-distortion optimization according to an embodiment, the apparatus initializes a value of first context information in a transform block to 1, and second context information in the transform block a context information initialization unit that initializes context information by initializing a value of 0; a transform coefficient quantization unit that scans transform coefficients in the transform block and quantizes the transform coefficients; a quantization level determining unit which calculates quantized level values of the transform coefficients and determines positions of some transform coefficients within the transform block or optimal quantized level values for subblocks within the transform block; and an optimum last significant coefficient determining unit for determining an optimal last significant coefficient for the subblock or the location of the partial transform coefficients, wherein scalar quantization using a dead zone is applied to the location of the partial transform coefficients or the subblock. A scalar quantization unit may be further included.

In the fast rate-distortion optimization-based quantization apparatus according to another embodiment, the transform coefficient quantizer scans transform coefficients in the transform block in a reverse diagonal order, and uses a quantization offset value of 0.5 to perform the transform This may include quantizing the coefficients.

In the fast rate-distortion optimization-based quantization apparatus according to another embodiment, the scalar quantization unit maps the transform coefficient to a discrete quantized level using a rounding operator, a sign, a quantization step size, and a quantization offset, , The dead zone is a period in which an input value is output as 0, and the quantization offset may include adjusting the period of the dead zone.

According to another embodiment, in the fast rate-distortion optimization-based quantization apparatus, the quantization level determiner fixes the last significant coefficient in the transform block and calculates rate-distortion costs for the remaining non-zero transform coefficients. may include doing

According to another embodiment, in the fast rate-distortion optimization-based quantization apparatus, the coefficient determiner initializes the transform block with an initial rate-distortion cost value, and uses the quantized level value in the transform block to generate an inverse diagonal It may involve scanning in order and calculating the rate-distortion cost.

The present invention can provide reducing the complexity of quantization. Therefore, the present invention has an effect of reducing the complexity of quantization.

The present invention can provide reducing the complexity of rate-distortion optimization based quantization. Therefore, the present invention has an effect of reducing the complexity of quantization based on rate-distortion optimization.

1 is a flowchart illustrating a quantization method based on fast rate-distortion optimization according to an embodiment.
2 is a block diagram illustrating a quantization apparatus based on fast rate-distortion optimization according to an embodiment.
3 illustrates a detailed configuration of a quantization apparatus based on high-speed rate-distortion optimization according to an embodiment.
4 illustrates an image decoding apparatus to which a quantization method based on high-speed rate-distortion optimization according to an embodiment is applied.
5 schematically illustrates a division structure of an image when encoding and decoding an image using a quantization method based on fast rate-distortion optimization according to an embodiment.
6 illustrates a shape of a prediction unit that may be included in a coding unit to which a quantization method based on fast rate-distortion optimization according to an embodiment is applied.
7 illustrates a form of a transform unit that may be included in a coding unit to which a quantization method based on fast rate-distortion optimization according to an embodiment is applied.
8 illustrates a step of determining an optimal quantized level value in a quantization method based on fast rate-distortion optimization according to an embodiment.
9 illustrates a step of determining an optimal last significant coefficient in a quantization method based on fast rate-distortion optimization according to an embodiment.
10 illustrates a transform block to which a quantization method based on fast rate-distortion optimization according to an embodiment is applied.
11 illustrates a quantization method based on fast rate-distortion optimization according to an embodiment.
12 illustrates a quantization method based on fast rate-distortion optimization using a transform block according to an embodiment.

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

1 is a flowchart illustrating a quantization method based on fast rate-distortion optimization according to an embodiment.

According to an embodiment, an image encoder, which is a high-speed rate-distortion optimization-based quantization device, may obtain a quantized transform coefficient level by performing quantization on transform coefficients in an image encoding process. In this case, quantizing the transform coefficient may be reducing the bit depth of the coefficient. Also, the quantized transform coefficient level may be a quantized level.

An image encoder, which is a high-speed rate-distortion optimization-based quantization device, uses a scalar quantization method using a deadzone or a rate-distortion optimized quantization method when performing quantization. can be used

In addition, the high-speed rate-distortion optimization-based quantization apparatus may optimize a tradeoff between a rate, which is a bit rate of encoded image data, and distortion, which is a difference between a reconstructed image and an original image.

Referring to FIG. 1 , a quantization method based on fast rate-distortion optimization performed by a quantization apparatus based on fast rate-distortion optimization consists of the following steps. Of course, in some cases, each step may be performed at the same time, or the order may be changed, but is not limited thereto.

In step S101, the fast rate-distortion optimization based quantization apparatus initializes the value of the first context information in the transform block to 1 and initializes the value of the second context information in the transform block to 0 to initialize the context information. can

For example, an image encoder, which is a high-speed rate-distortion optimization-based quantization device, initializes context information. In this case, the context information C1 value may be initialized to 1, and the C2 value may be initialized to 0.

In step S102, the fast rate-distortion optimization based quantization apparatus scans the transform coefficients in the transform block and quantizes the transform coefficients. In this case, quantization based on fast rate-distortion optimization may be performed by scanning transform coefficients in the transform block in a reverse diagonal order and quantizing the transform coefficients using a quantization offset value of 0.5.

For example, an image encoder, which is a high-speed rate-distortion optimization-based quantization device, may scan transform coefficients in a transform block to quantize transform coefficients and calculate quantized level values. Also, an image encoder, which is a fast rate-distortion optimization based quantization device, scans transform coefficients in a transform coefficient block in a reverse diagonal scan order. In this case, while scanning the transform coefficient, the transform coefficient may be quantized using a quantization offset value of 0.5, and a quantized level value may be calculated.

In step S103, the fast rate-distortion optimization based quantization apparatus calculates quantized level values of transform coefficients, and obtains optimal quantized level values for positions of some transform coefficients in a transform block or subblocks in a transform block. can decide

For example, an image encoder, which is a fast rate-distortion optimization based quantization device, may determine optimal quantized level values for transform coefficients in a transform coefficient block.

Specifically, the fast rate-distortion optimization based quantization apparatus may fix the last significant coefficient in the transform block.

Next, the fast rate-distortion optimization-based quantization apparatus may calculate rate-distortion costs for remaining non-zero transform coefficients.

In step S104, the fast rate-distortion optimization-based quantization apparatus may determine positions of some transform coefficients or optimal last significant coefficients for subblocks.

For example, an image encoder, which is a fast rate-distortion optimization based quantization device, may determine an optimal last significant coefficient.

Specifically, the fast rate-distortion optimization-based quantization apparatus may initialize the transform block with an initial rate-distortion cost value. Next, the fast rate-distortion optimization based quantization apparatus may scan in an inverse diagonal order using the quantized level values in the transform block and calculate the rate-distortion cost.

In addition, in steps between steps S101 and step S104, the fast rate-distortion optimization-based quantization apparatus may apply scalar quantization using a dead zone to positions of some transform coefficients or subblocks. In addition, in some cases, in a step prior to step S101, the fast rate-distortion optimization based quantization apparatus may apply scalar quantization using a dead zone to positions of some transform coefficients or subblocks.

In this case, the fast rate-distortion optimization-based quantization apparatus may map transform coefficients to discrete quantized levels using a rounding operator, a sign, a quantization step size, and a quantization offset.

Of course, the dead zone may be a section in which the input value is output as 0, and the quantization offset may adjust the section of the dead zone.

For example, the fast rate-distortion optimization based quantization apparatus may map transform coefficients into discrete quantized levels using Equation 1 below.

In this case, operator · is an operation for rounding to the nearest integer value, and Δ is a quantization step size. Also, f is a quantization offset, and the function sign() outputs a sign for an input value W. In this case, the quantization offset f may adjust the section of the dead zone, and z may be a quantized level value.

In addition, when an image decoder, which is a high-speed rate-distortion optimization based quantization device, performs inverse quantization, Equation 2 below can be used.

In this case, W' may be an inverse quantized coefficient, and Z may be a quantized level. The inverse quantization method performed by the high-speed rate-distortion optimization-based quantization apparatus can be used not only for the quantized level to which the rate-distortion optimization-based quantization method is applied, but also to the quantized level to which the scalar quantization method using the dead zone is applied.

2 is a block diagram illustrating a quantization apparatus based on fast rate-distortion optimization according to an embodiment.

Referring to FIG. 2 , the fast rate-distortion optimization based quantization apparatus 200 includes a context information initialization unit 210, a transform coefficient quantization unit 220, a quantization level determination unit 230, an optimal last significant coefficient determination unit ( 240) and a scalar quantization unit 250. At this time, the context information initialization unit 210, the transform coefficient quantization unit 220, the quantization level determination unit 230, the optimal last significant coefficient determination unit 240, and the scalar quantization unit 250 are an electronic circuit, an electric circuit, and an integrated circuit. It may be configured including a circuit. In addition, the context information initialization unit 210, the transform coefficient quantization unit 220, the quantization level determination unit 230, the optimal last significant coefficient determination unit 240, and the scalar quantization unit 250 include a processor, a memory, and a data transceiver. It may be configured to include, but is not limited thereto.

The context information initialization unit 210 may initialize the context information by initializing the value of the first context information in the conversion block to 1 and initializing the value of the second context information in the conversion block to 0.

The transform coefficient quantization unit 220 may scan transform coefficients in a transform block and quantize the transform coefficients. Also, the transform coefficient quantization unit 220 may scan the transform coefficients in the transform block in a reverse diagonal order and quantize the transform coefficients using a quantization offset value of 0.5.

The quantization level determiner 230 may calculate quantized level values of transform coefficients and determine positions of some transform coefficients within a transform block or optimal quantized level values for subblocks within a transform block. Also, the quantization level determiner 230 may fix the last significant coefficient in the transform block and calculate rate-distortion costs for the remaining non-zero transform coefficients.

The optimal last significant coefficient determiner 240 may determine the optimal last significant coefficient for a subblock or position of some transform coefficients. The optimal last significant coefficient determiner 240 may initialize the transform block with an initial rate-distortion cost value, scan the transform block in an inverse diagonal order using the quantized level values in the transform block, and calculate the rate-distortion cost.

The scalar quantization unit 250 may apply scalar quantization using a dead zone to positions of some transform coefficients or subblocks. In addition, the scalar quantization unit 250 may map transform coefficients to discrete quantized levels using a rounding operator, a sign, a quantization step size, and a quantization offset. In this case, the dead zone may be a section in which the input value is output as 0, and the quantization offset may adjust the section of the dead zone.

3 illustrates a detailed configuration of a quantization apparatus based on high-speed rate-distortion optimization according to an embodiment.

Referring to FIG. 3, it is a block diagram showing the configuration of an image encoding device, which is a quantization device based on high-speed rate-distortion optimization. The video encoding apparatus 300, which is a fast rate-distortion optimization based quantization apparatus, may include a motion estimation unit 311, a motion compensation unit 312, an intra prediction unit 320, and a switch 315. In addition, the video encoding device 300, which is a high-speed rate-distortion optimization based quantization device, includes a subtractor 325, a transform unit 330, a quantization unit 340, an entropy encoding unit 350, an inverse quantization unit 360, It may also include an inverse transform unit 370, an adder 375, a filter unit 380, and a reference picture buffer 390.

The image encoding apparatus 300, which is a high-speed rate-distortion optimization-based quantization device, may perform encoding on an input image in an intra mode or an inter mode and output a bitstream. Also, in the case of the intra mode, the switch 315 may be switched to the intra mode, and in the case of the inter mode, the switch 315 may be switched to the inter mode. Also, the image encoding apparatus 300, which is a fast rate-distortion optimization based quantization device, may generate a prediction block for an input block of an input image and then encode a residual between the input block and the prediction block.

In the case of the intra mode, the intra predictor 320 may generate a prediction block by performing spatial prediction using pixel values of previously encoded blocks adjacent to the current block. Also, in the case of the inter mode, the motion estimation unit 311 may obtain a motion vector by finding a region that best matches the input block in the reference picture stored in the reference picture buffer 390 during the motion estimation process. Also, the motion compensator 312 may generate a prediction block by performing motion compensation using a motion vector. In this case, the motion vector is a 2D vector used for inter prediction, and may indicate an offset between a current encoding and decoding target image and a reference image.

The subtractor 325 may generate a residual block by a difference between the input block and the generated prediction block.

The transform unit 330 may output a transform coefficient by performing transform on the residual block. Here, the transform coefficient may refer to a coefficient value generated by transforming a residual block or residual signal. Of course, in some cases, a quantized transform coefficient level generated by applying quantization to the transform coefficient may also be referred to as a transform coefficient.

The quantization unit 340 may quantize an input transform coefficient according to a quantization parameter and output a quantized transform coefficient level. At this time, the quantization unit 340 may quantize the input transform coefficient using a quantization matrix. In this case, the quantized transform coefficient level may be a quantized level or, in some cases, may be a part thereof as a transform coefficient.

The entropy encoding unit 350 may output a bit stream by performing entropy encoding based on the values calculated by the quantization unit 340 or the encoding parameter values calculated in the encoding process.

In this case, when entropy encoding is applied, a small number of bits are allocated to a symbol having a high probability of occurrence and a large number of bits are allocated to a symbol having a low probability of occurrence to represent the symbol. Of course, the size of bit streams for encoding target symbols can be reduced. In addition, compression performance of video encoding may be improved through entropy encoding.

In addition, the entropy encoding unit 350 derives a binarization method of the target symbol and a probability model of the target symbol or bin, and then performs arithmetic encoding using the derived binarization method or probability model. can also be performed. In addition, the entropy encoding unit 350 may use an encoding method such as exponential golomb, context-adaptive variable length coding (CAVLC), or context-adaptive binary arithmetic coding (CABAC) for entropy encoding.

Since the video encoding apparatus 300, which is a high-speed rate-distortion optimization based quantization apparatus according to an embodiment, performs inter-prediction encoding, that is, inter-prediction encoding, the currently encoded video needs to be decoded and stored to be used as a reference video. there is Also, the quantized level is inversely quantized by the inverse quantization unit 360 and inversely transformed by the inverse transformation unit 370 . In addition, the inverse quantized and inverse transformed coefficients are added to the prediction block through an adder 375, and a reconstructed block is generated.

The reconstructed block passes through a filter unit 380, and the filter unit 380 applies at least one of a deblocking filter, a sample adaptive offset (SAO), and an adaptive loop filter (ALF) to the reconstructed block or the reconstructed picture. can do. In this case, the filter unit 380 may be an adaptive in-loop filter. Also, the deblocking filter may remove block distortion generated at a boundary between blocks. SAO may add an appropriate offset value to a pixel value to compensate for a coding error. ALF may perform filtering based on a value obtained by comparing a reconstructed image with an original image. A reconstructed block that has passed through the filter unit 380 may be stored in the reference picture buffer 390.

4 illustrates an image decoding apparatus to which a quantization method based on high-speed rate-distortion optimization according to an embodiment is applied.

Referring to FIG. 4 , the video decoding apparatus 400 to which the fast rate-distortion optimization based quantization method is applied includes an entropy decoding unit 410, an inverse quantization unit 420, an inverse transform unit 430, and an intra prediction unit 440. , a motion compensation unit 450, an adder 455, a filter unit 460, and a reference picture buffer 470.

The image decoding apparatus 400 to which the high-speed rate-distortion optimization-based quantization method is applied receives the bitstream output by the encoder, which is a high-speed rate-distortion optimization-based quantization device, performs decoding in an intra mode or inter mode, and reconstructed image , that is, a restored image can be output. Specifically, in the intra mode, the switch may be switched to the intra mode, and in the case of the inter mode, the switch may be switched to the inter mode.

In addition, the video decoding apparatus 400 to which the fast rate-distortion optimization based quantization method is applied may obtain a reconstructed residual block from the input bitstream and generate a prediction block. Next, the video decoding apparatus 400 to which the fast rate-distortion optimization based quantization method is applied may generate a reconstructed block that is a reconstructed block by adding the reconstructed residual block and the prediction block.

The entropy decoding unit 410 may entropy-decode an input bitstream according to a probability distribution to generate symbols including symbols in a quantized level form. Also, the entropy decoding method may be performed as a reverse process of the entropy encoding.

The quantized level is inversely quantized in the inverse quantization unit 420 and inversely transformed in the inverse transform unit 430, and as a result of inverse quantization and inverse transformation of the quantized level, a reconstructed residual block may be generated. At this time, the inverse quantization unit 420 may apply a quantization matrix to the quantized level.

In the case of the intra mode, the intra prediction unit 440 may generate a prediction block by performing spatial prediction using pixel values of previously encoded blocks adjacent to the current block. In the inter mode, the motion compensation unit 450 may generate a prediction block by performing motion compensation using a motion vector and a reference image stored in the reference picture buffer 470 .

The reconstructed residual block and the predicted block are added by the adder 455, and the added block may pass through the filter unit 460. The filter unit 460 may apply at least one of a deblocking filter, SAO, and ALF to a reconstructed block or a reconstructed picture. The filter unit 460 may output a reconstructed image, that is, a reconstructed image. In some cases, the reconstructed image may be stored in the reference picture buffer 470 and used for inter prediction.

5 schematically illustrates a division structure of an image when encoding and decoding an image using a quantization method based on high-speed rate-distortion optimization according to an embodiment.

The high-speed rate-distortion optimization-based quantization apparatus may perform encoding and decoding with a coding unit (CU) in order to efficiently segment an image. A unit may be configured by combining a syntax element and a block including image samples. Also, a unit may be divided, and in this case, a block corresponding to the unit may be divided according to circumstances.

Referring to FIG. 5 , in HEVC, the high-speed rate-distortion optimization-based quantization apparatus sequentially divides an image 500 into units of largest coding units (LCUs), and then determines a division structure in units of LCUs. . In some cases, the division structure may represent a distribution of coding units (CUs) for efficiently encoding an image within the LCU 510 .

In this case, the fast rate-distortion optimization-based quantization apparatus may determine whether to divide the distribution of CUs into four CUs reduced by half of the horizontal and vertical sizes of one CU. Of course, the divided CU may be recursively divided into four CUs in which the horizontal and vertical sizes of the divided CU are reduced by half in the same way.

In addition, the fast rate-distortion optimization-based quantization apparatus may recursively divide the division of the CU to a predefined depth. In this case, the depth information is information indicating the size of the CU and may be stored for each CU.

For example, the depth of LCU may be 0, and the depth of Smallest Coding Unit (SCU) may be a predefined maximum depth. Of course, as described above, the LCU is a coding unit having the largest coding unit size, and the smallest coding unit (SCU) may be a coding unit having the smallest coding unit size.

The depth of the CU increases by 1 whenever division is performed from the LCU 510 to half of the horizontal and vertical sizes. In addition, for each depth, a CU that does not perform division has a size of 2Nx2N, and a CU that performs division has a size of 2Nx2N to four CUs having a size of NxN. Can be divided into. In this case, the size of N may decrease by half whenever the depth increases by 1.

Also, for example, the size of an LCU having a minimum depth of 0 may be 64x64 pixels, and the size of an SCU having a maximum depth of 3 may be 8x8 pixels. In this case, a CU (LCU) of 64x64 pixels may be expressed as depth 0, a CU of 32x32 pixels may be expressed as depth 1, a CU of 16x16 pixels may be expressed as depth 2, and a CU (SCU) of 8x8 pixels may be expressed as depth 3.

Also, information on whether to divide a specific CU may be expressed through 1-bit division information for each CU. This division information may be included in all CUs except for the SCU. In some cases, if the quantization device based on fast rate-distortion optimization does not divide the CU, 0 may be stored in the division information, and quantization based on fast rate-distortion optimization When the device divides the CU, it may store 1 in division information.

6 illustrates a shape of a prediction unit that may be included in a coding unit to which a quantization method based on fast rate-distortion optimization according to an embodiment is applied.

Referring to FIG. 6 , the shape of a prediction unit (PU) that can be included in the coding unit (CU) can be known. The fast rate-distortion optimization-based quantization apparatus may divide a CU that is not further divided among CUs divided from the LCU into one or more prediction units.

At this time, the prediction unit is a basic unit for performing prediction, and is encoded and decoded by a quantization device based on high-speed rate-distortion optimization as any one of a skip mode, an inter mode, and an intra mode, and each Depending on the mode, it may be partitioned in various forms. For example, in the case of the skip mode, the fast rate-distortion optimization-based quantization apparatus may support a 2Nx2N mode 410 having the same size as the CU without a partition within the CU.

In addition, in the case of inter mode, the fast rate-distortion optimization based quantization apparatus can support 8 types of partitions within a CU. For example, the fast rate-distortion optimization based quantization apparatus includes 2Nx2N mode 410, 2NxN mode 415, Nx2N mode 420, NxN mode 425, 2NxnU mode 430, 2NxnD mode 435, nLx2N mode 440 and nRx2N mode 445 may be supported. In addition, the fast rate-distortion optimization based quantization apparatus may support 2Nx2N mode 410 and NxN mode 425 within the CU in the case of intra mode.

7 illustrates a form of a transform unit that may be included in a coding unit to which a quantization method based on fast rate-distortion optimization according to an embodiment is applied.

Referring to FIG. 7 , the shape of a transform unit (TU) that can be included in a coding unit (CU) can be known. In this case, the transform unit may be a basic unit used for transformation, quantization, inverse transformation, and inverse quantization processes within the CU. Also, a TU may have a square or rectangular shape.

Among the CUs divided from the LCU by the fast rate-distortion optimization-based quantization apparatus, CUs that are not further divided may be divided into one or more TUs by the fast rate-distortion optimization-based quantization apparatus. In this case, the division structure of the TU may be a quad-tree structure. For example, in some cases, one CU 710 may be divided into TUs of various sizes by being divided one or more times according to a quad tree structure.

8 illustrates a step of determining an optimal quantized level value in a quantization method based on fast rate-distortion optimization according to an embodiment.

Referring to FIG. 8 , a step of determining an optimal quantized level value in a quantization method based on fast rate-distortion optimization performed by a quantization apparatus based on fast rate-distortion optimization is performed as follows.

In step S801, the video encoder, which is a fast rate-distortion optimization based quantization device, may fix the last significant coefficient in the transform block. In this case, the significant coefficient may be a quantized level value having a non-zero value, and the last significant coefficient may be present at the last position among quantized levels having a non-zero value in the transform block. Also, the last significant coefficient may be the first coefficient whose quantized level value is not 0 when transform coefficients are scanned and quantized in an inverse diagonal scan order.

In step S801, the video encoder, which is a fast rate-distortion optimization based quantization device, determines the quantized levels of each transform coefficient for the remaining transform coefficients whose value is not 0 except for the last important coefficient in the transform block. We can calculate the rate-distortion cost for In this case, the video encoder, which is a high-speed rate-distortion optimization based quantization device, determines an optimal quantization level based on the calculated rate-distortion cost. Of course, the remaining transform coefficients may be transform coefficients that occur next to the last significant coefficient in the inverse diagonal scan order.

Specifically, an image encoder, which is a high-speed rate-distortion optimization-based quantization device, first determines whether the quantized level value for a corresponding transform coefficient is less than 3 in order to determine an optimal quantized level for one transform coefficient do.

At this time, when the quantized level value for the corresponding transform coefficient is less than 3, the video encoder, which is a high-speed rate-distortion optimization based quantization device, replaces the corresponding transform coefficient with a Level value, a Level -1 value, and a value of 0 After that, we can calculate the rate-distortion cost for each. At this time, the level value may be the quantized level value calculated in step 2 above. In addition, when the quantized level value for the corresponding transform coefficient is greater than 3, the video encoder, which is a high-speed rate-distortion optimization based quantization device, replaces the corresponding transform coefficient with a Level value and a value of 0, and then rate-distortion for each cost can be calculated.

The video encoder, which is a fast rate-distortion optimization based quantization device, can update the rate-distortion cost. In this case, the video encoder, which is a high-speed rate-distortion optimization-based quantization device, may update the rate-distortion cost for encoding to 0 as quantized level values for all transform coefficients in the transform block. Also, the video encoder, which is a fast rate-distortion optimization-based quantization device, may update a rate-distortion cost for encoding each transform coefficient in a transform block. In addition, an image encoder, which is a fast rate-distortion optimization based quantization device, may update a rate-distortion cost for encoding a significant map of a transform block. In this case, the importance map may be information about whether a quantized level value for each transform coefficient in the transform block is 0 or not.

An image encoder, which is a fast rate-distortion optimization based quantization device, may update context information about an optimal quantization level for a transform coefficient. Also, the image encoder, which is a fast rate-distortion optimization based quantization device, may determine a quantized level having the smallest rate-distortion cost among quantized levels as an optimal quantized level. In this case, the updated context information may be used when determining the quantized level of the next transform coefficient.

In some cases, the video encoder, which is a fast rate-distortion optimization-based quantization device, may perform step S802 on remaining transform coefficients that are not 0 in the transform block.

9 illustrates a step of determining an optimal last significant coefficient in a quantization method based on fast rate-distortion optimization according to an embodiment.

Referring to FIG. 9 , the step of determining the optimal last significant coefficient in the fast rate-distortion optimization based quantization method performed by the fast rate-distortion optimization based quantization apparatus can be performed as follows.

In step S901, the video encoder, which is a fast rate-distortion optimization based quantization device, converts the rate-distortion cost value, d64BestCost, into a rate-distortion cost value when the transform block is not encoded. can be initialized.

In step S902, the video encoder, which is a fast rate-distortion optimization based quantization device, scans in an inverse diagonal scan order until the quantized level value in the transform block meets a transform coefficient greater than 1, and rate-distortion cost can be calculated

Specifically, the video encoder, which is a high-speed rate-distortion optimization-based quantization device, considers a transform coefficient having a quantized level value greater than 1 as the last significant coefficient while scanning according to step S902, and rate-distortion cost for the entire transform block totalCost can be calculated.

Next, if totalCost is less than d64BestCost, the video encoder, which is a fast rate-distortion optimization based quantization device, sets the position of the last significant coefficient to iBestLastIdxP1 indicating the optimal last position indicator, and sets d64BestCost to totalCost.

In this case, when the video encoder, which is a high-speed rate-distortion optimization-based quantization device, calculates the rate-distortion cost, the rate-distortion cost can be calculated using Equation 3 below. D may be a mean square error of difference values between original transform coefficients and restored transform coefficients in a transform block, and R may be a bit rate using related context information.

10 illustrates a transform block to which a quantization method based on fast rate-distortion optimization according to an embodiment is applied.

According to an embodiment, the fast rate-distortion optimization based quantization apparatus can reduce the complexity of rate-distortion optimization based quantization. In this case, the fast rate-distortion optimization-based quantization apparatus may determine an optimal quantized level and an optimal last significant coefficient only for some transform coefficient positions or subblocks within a transform block.

In addition, the high-speed rate-distortion optimization-based quantization apparatus applies a scalar quantization method using a dead zone only to some transform coefficient positions or sub-blocks within a transform block, and rate-distortion optimization-based quantization is applied to the entire transform block according to the applied result. method can be applied.

Referring to FIG. 10 , a transform block may have a size of 4x4, 8x8, 16x16, or 32x32 according to the transform size. In this case, the high-speed rate-distortion optimization-based quantization apparatus may use inverse diagonal scan, inverse horizontal scan, inverse vertical scan, or the like to scan each transform block.

In addition, a transform block having a size of 8x8 or larger may be divided into 4x4 sub-blocks. 9 below shows an 8x8 transform block having a 4x4 transform block and four 4x4 unit subblocks. In the 4x4 transform block, transform coefficients or quantized levels may be scanned in an inverse diagonal scan order as shown. In addition, in an 8x8 transform block, transform coefficients or quantized levels within subblocks may be scanned in an inverse diagonal scan order as shown, and scans between subblocks in a transform block may also be scanned in an inverse diagonal scan order as shown. there is. In this case, the scan method within the transform block and subblock and the scan between subblocks is not limited to the inverse diagonal scan method, and the aforementioned inverse horizontal scan and inverse vertical scan scan methods may also be used. Of course, rate-distortion optimization-based quantization may be performed in units of transform blocks by the high-speed rate-distortion optimization-based quantization apparatus.

11 illustrates a quantization method based on fast rate-distortion optimization according to an embodiment.

When the transform is performed by the fast rate-distortion optimization-based quantization device, the transform coefficient is generated at the upper left corner of the transform block, and the transform coefficient generated at the upper left corner after transform may have a relatively low frequency. In addition, the conversion coefficient generated on the lower right side may be a high frequency. Of course, since the low-frequency component has more influence on the coding efficiency than the high-frequency component, the low-frequency component may play a more important role than the high-frequency component in the image encoder and decoder, which are fast rate-distortion optimization-based quantization devices.

Referring to FIG. 11, a total of 16 transform coefficients may exist in a 4x4 transform block, a total of 64 transform coefficients in an 8x8 transform block, a total of 256 transform coefficients in a 16x16 transform block, and a total of 1024 transform coefficients in a 32x32 transform block. may exist.

In this case, when the rate-distortion optimization-based quantization method performed by the high-speed rate-distortion optimization-based quantization device is used, the optimal quantized level and the optimal last significant coefficient must be determined for up to 1024 transform coefficients for a 32x32 transform block. . Of course, in some cases, the complexity of an image coder, which is a fast rate-distortion optimization based quantization device, may increase considerably, so a fast rate-distortion optimization based quantization device can prevent this.

In the fast rate-distortion optimization-based quantization apparatus, in order to reduce the optimal quantized level and the number of optimal last significant coefficients to be determined in the transform block, some transform coefficient positions 1101 or subblocks 1102 in the transform block ) can determine the optimal quantized level and the optimal last significant coefficient.

For example, a quantization apparatus based on fast rate-distortion optimization determines an optimal quantized level and an optimal last significant coefficient only for transform coefficients present in some transform coefficient positions in a transform block or in some subblocks according to the size of a transform block. can decide In this case, the fast rate-distortion optimization-based quantization apparatus may set all quantized level values to 0 for transform coefficients that do not correspond to positions of some transform coefficients in a transform block or to some subblocks.

In some cases, the fast rate-distortion optimization-based quantization apparatus may determine an optimal quantized level and an optimal last significant coefficient for only some transform coefficient positions 1101 for an 8x8 transform block. In some cases, the fast rate-distortion optimization-based quantization apparatus may determine an optimal quantized level and an optimal last significant coefficient for only some subblocks 1102 for a 16x16 transform block.

Of course, the high-speed rate-distortion optimization-based quantization apparatus can determine the optimal quantized level and the optimal last significant coefficient only for transform coefficients present in some transform coefficient positions or in some subblocks, similarly to 4x4 transform blocks and 32x32 transform blocks. there is. In addition, for 8x8, 16x16, or 32x32 transform blocks, the high-speed rate-distortion optimization-based quantization device, according to the inverse horizontal scan, inverse vertical scan, or inverse diagonal scan order, which is the scan order between subblocks, each two from the last scan order, The above method may be performed only for 9 or 35 subblocks.

12 illustrates a quantization method based on fast rate-distortion optimization using a transform block according to an embodiment.

According to an embodiment, the fast rate-distortion optimization-based quantization apparatus optimizes only transform coefficients existing in some transform coefficient positions in a transform block or in some subblocks according to an intra-prediction mode of a prediction unit including the transform block. The quantized level and optimal last significant coefficient can be determined. The high-speed rate-distortion optimization-based quantization apparatus, when the intra-prediction mode is a vertical mode or a mode having a direction similar to the vertical mode, obtains an optimal quantized level and an optimal quantization level only for the top two subblocks in an 8x8 transform block. The final important factor can be determined. In addition, the fast rate-distortion optimization-based quantization apparatus, when the intra-prediction mode is a horizontal mode or a mode having a direction similar to the horizontal mode, optimal quantized levels only for two subblocks existing on the left side of an 8x8 transform block and an optimal last significant coefficient.

According to an embodiment, the fast rate-distortion optimization based quantization apparatus performs optimal quantization only for transform coefficients present in some transform coefficient positions in a transform block or in some subblocks according to a prediction mode of a coding unit including the transform block. level and optimal last critical factor can be determined.

Also, when the prediction mode is the intra prediction mode, many transform coefficients may exist in a relatively low frequency region than in the inter prediction mode. In this case, in the case of the intra prediction mode, the fast rate-distortion optimization based quantization apparatus may perform the above method for more transform coefficient positions or subblocks in the low-frequency region than in the inter prediction mode.

According to an embodiment, the high-rate-distortion optimization-based quantization apparatus obtains an optimal quantized level only for transform coefficients present in some transform coefficient positions in a transform block or in some subblocks according to a luminance component or a chrominance component that is a color component, and The optimal last critical factor can be determined. Since transform coefficients are more likely to exist in the luminance component than chrominance components, the fast rate-distortion optimization-based quantization apparatus has more transform coefficient positions or subblocks in the transform block for the luminance component than in the transform block for the chrominance component. The above method can be performed.

In addition, the high-speed rate-distortion optimization-based quantization apparatus does not apply rate-distortion optimization-based quantization to the entire transform block, unlike the conventional rate-distortion optimization-based quantization method. The high-speed rate-distortion optimization-based quantization device applies the scalar quantization method using the dead zone mainly to the low-frequency component, which is a relatively important component in the transform block, and then rate-distortion optimization when there is a quantized level value for the low-frequency component. Apply base quantization. In addition, the fast rate-distortion optimization-based quantization apparatus may determine quantized level values for all transform coefficients in a corresponding transform block as 0 when there is no quantized level value for a low-frequency component.

At this time, the high-speed rate-distortion optimization-based quantization apparatus applies a scalar quantization method using a dead zone only to some transform coefficient positions or subblocks within a transform block, and if a quantized level value exists in the corresponding transform coefficient position or subblock Quantization based on rate-distortion optimization can be applied to In addition, the fast rate-distortion optimization-based quantization apparatus may determine quantized level values for all transform coefficients in the corresponding transform block as 0 when there is no quantized level value in the corresponding transform coefficient position or subblock.

Referring to FIG. 12 , a transform coefficient position 1201 or a sub-block 1202 to which scalar quantization using a dead zone is applied may be determined according to the size of a transform block by a quantization apparatus based on fast rate-distortion optimization. In this case, the fast rate-distortion optimization-based quantization apparatus may apply scalar quantization using a dead zone to the position 1201 or sub-block 1202 and determine whether to apply rate-distortion optimization-based quantization to the entire transform block.

According to an embodiment, the transform coefficient position 1201 or the subblock 1202 to which scalar quantization using a dead zone is applied may be determined according to a prediction mode of a coding unit by a quantization apparatus based on fast rate-distortion optimization. For example, when the prediction mode is the intra prediction mode, more transform coefficients may exist in a relatively low frequency region than the inter prediction mode. In this case, in the case of the intra prediction mode, the high-speed rate-distortion optimization based quantization apparatus converts more transform coefficient positions or subblocks in the low frequency region than in the inter prediction mode to the region to which the scalar quantization method using the dead zone is applied. can decide

Also, according to an embodiment, a transform coefficient position 1201 or a subblock 1202 to which a scalar quantization method using a dead zone is applied may be determined according to a luminance component or a chrominance component that is a color component. At this time, since there is a higher possibility that transform coefficients exist in the luminance component than chrominance components, the fast rate-distortion optimization-based quantization apparatus places more transform coefficient positions or subblocks in the transform block for the luminance component than in the transform block for the chrominance component. A scalar quantization method using a dead zone may be determined as an area to be applied.

The devices described above may be implemented as hardware components, software components, and/or a combination of hardware components and software components. For example, devices and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA), It may be implemented using one or more general purpose or special purpose computers, such as a programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions. A processing device may run an operating system (OS) and one or more software applications running on the operating system. A processing device may also access, store, manipulate, process, and generate data in response to execution of software. For convenience of understanding, there are cases in which one processing device is used, but those skilled in the art will understand that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it can include. For example, a processing device may include a plurality of processors or a processor and a controller. Other processing configurations are also possible, such as parallel processors.

Software may include a computer program, code, instructions, or a combination of one or more of the foregoing, which configures a processing device to operate as desired or processes independently or collectively. You can command the device. Software and/or data may be any tangible machine, component, physical device, virtual equipment, computer storage medium or device, intended to be interpreted by or provide instructions or data to a processing device. , or may be permanently or temporarily embodied in a transmitted signal wave. Software may be distributed on networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer readable media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer readable medium. The computer readable medium may include program instructions, data files, data structures, etc. alone or in combination. Program commands recorded on the medium may be specially designed and configured for the embodiment or may be known and usable to those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. - includes hardware devices specially configured to store and execute program instructions, such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of program instructions include high-level language codes that can be executed by a computer using an interpreter, as well as machine language codes such as those produced by a compiler. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

As described above, although the embodiments have been described with limited examples and drawings, those skilled in the art can make various modifications and variations from the above description. For example, the described techniques may be performed in an order different from the method described, and/or components of the described system, structure, device, circuit, etc. may be combined or combined in a different form than the method described, or other components may be used. Or even if it is replaced or substituted by equivalents, appropriate results can be achieved. Therefore, other implementations, other embodiments, and equivalents of the claims are within the scope of the following claims.

Claims (3)

In the video decoding method,
obtaining a residual signal for the current transform block;
performing inverse quantization on a residual signal of the current transform block; and
Including performing inverse transform on the inverse quantized signal of the current transform block,
The residual signal is obtained from a bitstream based on entropy decoding,
The residual signal is obtained only for a partial region of the current transform block;
The partial region is determined based on the size of the current transform block.
In the video encoding method,
deriving a residual signal for the current transform block;
performing transformation on a residual signal of the current transformation block;
performing quantization on the transformed signal of the current transform block; and
Encoding the quantized signal of the current transform block,
The encoding is performed based on entropy encoding,
Only a partial region of the quantized signal of the current transform block is coded;
The partial region is determined based on the size of the current transform block.
A computer-readable recording medium storing a bitstream generated by an image encoding method,
The video encoding method,
deriving a residual signal for the current transform block;
performing transformation on a residual signal of the current transformation block;
performing quantization on the transformed signal of the current transform block; and
Encoding the quantized signal of the current transform block,
Only a partial region of the quantized signal of the current transform block is coded;
The encoding is performed based on entropy encoding,
Characterized in that the partial area is determined based on the size of the current conversion block, computer readable recording medium.

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