KR20160088086A - Method for quantization based on fast rate-distortion optimization, and apparatus for the same - Google Patents

Abstract

The present invention relates to a method for quantization based on fast rate-distortion optimization, which is performed by an apparatus for quantization based on fast rate-distortion optimization. According to the present invention, provided is the method for quantization based on fast rate-distortion optimization, the method comprising the steps of: initializing context information by initializing a value of first context information in a transformation block to one and by initializing a value of second context information in the transformation block to zero; scanning transformation coefficients in the transformation block and quantizing the transformation coefficients; calculating quantized level values of the transformation coefficients, and determining optimal quantized level values for locations of some transformation coefficients in the transformation block or for sub-blocks in the transformation block; and determining optimal last significant coefficients for the locations of the some transformation coefficients or for the sub-blocks; and further comprising the step of applying scalar quantization using a dead zone to the locations of the some transformation coefficients or for the sub-blocks. According to the present invention, complexity of quantization can be reduced.

Description

[0001] METHOD AND APPARATUS FOR QUANTUMIZATION BASED ON FAST RATE-DISTORTION OPTIMIZATION, AND APPARATUS FOR THE SAME [0002]

The present invention relates to a fast rate-distortion optimization based quantization method and apparatus.

Recently, broadcasting service having high definition (HD) resolution has been expanded not only in domestic but also in the world, and many users are getting used to high resolution and high quality video.

Accordingly, many organizations are spurring the development of next generation video equipment. In addition, with the increase of interest in UHD (Ultra High Definition) having resolution more than four times of HDTV in addition to HDTV, compression technology for higher resolution and higher image quality is required.

Also, in order to compress an image, an inter prediction technique for predicting a pixel value included in a current picture from temporally preceding or succeeding pictures, prediction of a pixel value included in the current picture using pixel information in the current picture Intra prediction techniques may be used. For image compression, an entropy encoding technique may be used, in which a short code is assigned to a symbol having a high appearance frequency and a long code is assigned to a symbol having a low appearance frequency.

However, existing techniques have a problem that the complexity of quantization is large. Therefore, we propose a technique that can reduce the complexity of quantization.

The present invention aims at solving all of the above problems.

It is another object of the present invention to provide a method for reducing the complexity of quantization.

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

In order to accomplish the objects of the present invention as described above and achieve the characteristic effects of the present invention described below, the characteristic structure of the present invention is as follows.

In a high-speed rate-distortion optimization-based quantization method performed by a fast rate-distortion optimization-based quantization apparatus according to an embodiment, a fast rate-distortion optimization-based quantization method performed by a fast rate- Initializing a value of a first context information in a block to 1, and initializing a value of a second context information in the transform block to 0 to initialize context information; Scanning the transform coefficients in the transform block and quantizing the transform coefficients; Calculating a quantized level value of the transform coefficients, determining a position of some transform coefficients in the transform block or an optimal quantized level value for a subblock in the transform block; And determining an optimal last significant coefficient for the position of the transform coefficient or the subblock, and applying scalar quantization using the dead zone to the position of the transform coefficient or the subblock .

According to another embodiment, a fast rate-distortion optimization-based quantization method performed by a fast rate-distortion optimization-based quantization apparatus, the quantization step comprises scanning the transform coefficients in the transform block in a reverse diagonal order And quantizing the transform coefficients using a quantization offset value of 0.5.

According to yet another embodiment, a fast rate-distortion optimization based quantization method performed by a fast rate-distortion optimization based quantization apparatus, the step of applying the scalar quantization comprises: a rounding operator, a sign, a quantization step size, The mapping unit maps the transform coefficients to a discrete quantized level using a quantization offset, the dead zone is a period for outputting an input value as 0, and the quantization offset may include adjusting a duration of the dead zone .

In a fast rate-distortion optimization-based quantization method performed by a fast rate-distortion optimization based quantization apparatus according to yet another embodiment, the step of determining the optimal quantized level value comprises: ; And calculating a rate-distortion cost for the remaining non-zero transform coefficients.

According to yet another embodiment, a fast rate-distortion optimization-based quantization method performed by a fast rate-distortion optimization based quantization apparatus, the step of determining the optimal last significant coefficient comprises: Initializing with a cost value; And scaling in an inverse diagonal sequence using the quantized level values in the transform block and calculating the rate-distortion cost.

According to one embodiment, in a fast rate-distortion optimization-based quantization apparatus, a fast rate-distortion optimization-based quantization apparatus initializes a value of first context information in a transform block to 1, A context information initialization unit for initializing the context information to 0; A transform coefficient quantizer for scanning the transform coefficient in the transform block and quantizing the transform coefficient; A quantization level determination unit for calculating a quantized level value of the transform coefficient and determining a position of a transform coefficient in the transform block or an optimal quantized level value for a subblock in the transform block; And an optimal last significant coefficient determiner for determining a position of the transform coefficient or an optimum last significant coefficient for the subblock, wherein scalar quantization using the dead zone is applied to the position of the partial transform coefficient or the subblock And a scalar quantization unit.

According to another embodiment, in the fast rate-distortion optimization-based quantization apparatus, the transform coefficient quantization unit scans the transform coefficients in the transform block in a reverse diagonal order, and uses the quantization offset value 0.5 to perform the transform And quantizing the coefficients.

According to another embodiment, in the fast rate-distortion optimization-based quantization apparatus, the scalar quantization unit maps the transform coefficients 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 for outputting an input value as 0, and the quantization offset may include adjusting a period of the dead zone.

In another aspect of the present invention, the quantization level determination unit determines a final significance coefficient in the transform block and calculates a rate-distortion cost for non-zero transform coefficients Lt; / RTI >

In a high-speed rate-distortion optimization-based quantization apparatus according to another embodiment, the coefficient determination unit initializes the initial rate-distortion cost value for the transform block, and uses the quantized level value in the transform block to calculate an inverse diagonal In order, and calculating the rate-distortion cost.

The present invention can provide a reduction in the complexity of the quantization. Therefore, the present invention has the effect of reducing the complexity of the quantization.

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

1 is a flow chart illustrating a fast rate-distortion optimization based quantization method in accordance with an embodiment.
2 is a block diagram illustrating a fast rate-distortion optimization based quantization apparatus in accordance with an embodiment.
3 is a block diagram of a high-speed rate-distortion optimization-based quantization apparatus according to an exemplary embodiment of the present invention.
FIG. 4 illustrates an image decoding apparatus to which a fast rate-distortion optimization-based quantization method according to an embodiment is applied.
FIG. 5 schematically shows a divided structure of an image when encoding and decoding an image according to a fast rate-distortion optimization-based quantization method according to an embodiment.
FIG. 6 illustrates a prediction unit type that an encoding unit to which a fast rate-distortion optimization-based quantization method according to an embodiment of the present invention is applied.
FIG. 7 illustrates a conversion unit included in an encoding unit to which a fast rate-distortion optimization-based quantization method according to an embodiment is applied.
FIG. 8 illustrates determining the optimal quantized level value among the fast rate-distortion optimization-based quantization methods according to one embodiment.
FIG. 9 illustrates a step of determining an optimal last significant coefficient among the fast rate-distortion optimization-based quantization methods according to an embodiment.
FIG. 10 shows a transform block to which a fast rate-distortion optimization-based quantization method according to an embodiment is applied.
11 illustrates a fast rate-distortion optimization based quantization method according to an embodiment.
12 illustrates a fast rate-distortion optimization based quantization method using a transform block according to an embodiment.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a flow chart illustrating a fast rate-distortion optimization based quantization method in accordance with an embodiment.

According to one embodiment, an image encoder, which is a fast rate-distortion optimization-based quantization apparatus, can quantize a transform coefficient in an image encoding process to obtain a quantized transform coefficient level. At this time, quantizing the transform coefficient may be to reduce the bit depth of the coefficient. In addition, the quantized transform coefficient level may be a quantized level.

The image encoder, which is a fast rate-distortion optimization-based quantization apparatus, uses a scalar quantization method using a deadzone or a rate-distortion optimized quantization method using a quantization method Can be used.

In addition, the fast rate-distortion optimization-based quantization apparatus can optimize the tradeoff between the bit rate of the encoded image data and the distortion, which is the difference between the reconstructed image and the original image.

Referring to FIG. 1, a fast rate-distortion optimization-based quantization method performed by a fast rate-distortion optimization-based quantization apparatus is composed of the following steps. Of course, as the case may be, each step may be performed at the same time, and 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, initializes the value of the second context information in the transform block to 0, and initializes the context information .

For example, an image encoder which is a quantization apparatus based on a high-speed rate-distortion optimization initializes context information. At this time, 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 device may scan the transform coefficients in the transform block and quantize the transform coefficients. At this time, the transform coefficients in the transform block may be scanned in a reverse diagonal order, and quantization based on the fast rate-distortion optimization using the quantization offset value 0.5 to quantize the transform coefficients.

For example, an image encoder, which is a high-speed rate-distortion optimization-based quantization apparatus, can quantize transform coefficients and calculate quantized level values while scanning transform coefficients in a transform block. Also, the image encoder, which is a fast rate-distortion optimization-based quantization device, scans the transform coefficients in the transform coefficient block in a reverse diagonal scan order. At this time, it is possible to quantize the transform coefficient and calculate the quantized level value using the quantization offset value 0.5 while scanning the transform coefficient.

In step S103, the high-speed rate-distortion optimization-based quantization apparatus calculates a quantized level value of the transform coefficients, calculates an optimum quantized level value for the position of some transform coefficients in the transform block or a subblock in the transform block Can be determined.

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

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

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

In step S104, the fast rate-distortion optimization-based quantization device may determine an optimal last significant coefficient for the position or sub-block of some transform coefficients.

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

Specifically, a fast rate-distortion optimization-based quantization device may initialize to an initial rate-distortion cost value for a transform block. Next, the fast rate-distortion optimization-based quantization device can scan in reverse diagonal order using the quantized level values in the transform block and calculate the rate-distortion cost.

Further, in the steps between steps S101 to S104, the fast rate-distortion optimization-based quantization apparatus can apply scalar quantization using the dead zone to the position or sub-block of some transform coefficients. Also, as the case may be, in a stage prior to step S101, the fast rate-distortion optimization-based quantization apparatus may apply scalar quantization using the dead zone to the position or sub-block of some transform coefficients.

At this time, the fast rate-distortion optimization-based quantization apparatus can map the transform coefficients to a discrete quantized level using a rounding operator, a sign, a quantization step size, and a quantization offset.

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

For example, the fast rate-distortion optimization-based quantization apparatus can map the transform coefficients to a discrete quantized level using Equation (1) below.

At this time, the 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 the input value W. At this time, the quantization offset f can adjust the duration of the dead zone, and z can be a quantized level value.

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

Where W 'is an inverse quantized coefficient and Z can be a quantized level. The inverse quantization method performed by the fast 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 for the quantized level to which the scalar quantization method using the dead zone is applied.

2 is a block diagram illustrating a fast rate-distortion optimization based quantization apparatus in accordance with an embodiment.

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 optimum final significance 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 optimum final significance coefficient determination unit 240, and the scalar quantization unit 250 may be electronic circuits, Circuit. ≪ / RTI > The context information initialization unit 210, the transformation coefficient quantization unit 220, the quantization level determination unit 230, the optimum final significance coefficient determination unit 240, and the scalar quantization unit 250 may be implemented as a processor, a memory, and a data transceiver 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 transform block to 1 and initializing the value of the second context information in the transform block to zero.

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

The quantization level determination unit 230 may calculate the quantized level value of the transform coefficient and determine the position of some transform coefficients in the transform block or the optimal quantized level value for the subblock in the transform block. In addition, the quantization level determination unit 230 may fix the last significant coefficient in the transform block and calculate the rate-distortion cost for the remaining non-zero transform coefficients.

The optimum last significant coefficient determination unit 240 can determine an optimum last significant coefficient for a position or a sub-block of some transform coefficients. The optimum last significant coefficient determination unit 240 may initialize the initial rate-distortion cost value for the transform block, scan in an inverse diagonal sequence using the quantized level values in the transform block, and calculate the rate-distortion cost.

The scalar quantization unit 250 can apply scalar quantization using the dead zone to the position or sub-block of some transform coefficients. The scalar quantization unit 250 may map the transform coefficients to a discrete quantized level using a rounding operator, a sign, a quantization step size, and a quantization offset. In this case, the dead zone may be a section outputting the input value as 0, and the quantization offset may adjust the section of the dead zone.

3 is a block diagram of a high-speed rate-distortion optimization-based quantization apparatus according to an exemplary embodiment of the present invention.

3 is a block diagram illustrating a configuration of an image encoding apparatus that is a fast rate-distortion optimization-based quantization apparatus. The image encoding apparatus 300 that is a fast rate-distortion optimization-based quantization apparatus may include a motion prediction unit 311, a motion compensation unit 312, an intra prediction unit 320, and a switch 315. The image encoding apparatus 300 that is a fast rate-distortion optimization-based quantization apparatus includes a subtractor 325, a transform unit 330, a quantization unit 340, an entropy encoding unit 350, an inverse quantization unit 360, 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 fast rate-distortion optimization-based quantization apparatus, can perform encoding in an intra mode or an inter mode with respect to an input image and output a bit stream. In the case of the intra mode, the switch 315 is switched to the intra mode, and when the inter mode is selected, the switch 315 can be switched to the inter mode. Also, the image encoding apparatus 300, which is a fast rate-distortion optimization-based quantization apparatus, can generate a prediction block for an input block of an input image, and then code residuals between the input block and the prediction block.

In the intra mode, the intraprediction unit 320 may generate a prediction block by performing spatial prediction using the pixel values of the already coded blocks around the current block. In the inter mode, the motion predicting unit 311 can find a motion vector by searching an area of the reference picture stored in the reference picture buffer 390, which is best matched with the input block, in the motion estimation process. Also, the motion compensation unit 312 can generate a prediction block by performing motion compensation using a motion vector. In this case, the motion vector is a two-dimensional vector used for inter prediction, and can represent the offset between the current image to be encoded and decoded and the reference image.

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

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

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

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

At this time, when entropy encoding is applied, a small number of bits are assigned 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, so that a symbol can be represented. Of course, the size of the bit stream for the symbols to be encoded can be reduced. In addition, the compression performance of image encoding can be enhanced through entropy encoding.

The entropy encoding unit 350 may derive a binarization method of the object symbol and a probability model of the object symbol or bin and then perform arithmetic coding using the derived binarization method or probability model . In addition, the entropy encoding unit 350 may use an encoding method such as exponential golomb, context-adaptive variable length coding (CAVLC), and context-adaptive binary arithmetic coding (CABAC) for entropy encoding.

Since the image encoding apparatus 300, which is a fast rate-distortion optimization-based quantization apparatus according to an embodiment, performs inter-prediction encoding, i.e., inter-prediction encoding, the currently encoded image needs to be decoded and stored to be used as a reference image. . The quantized level is inversely quantized by the inverse quantization unit 360 and inversely transformed by the inverse transformation unit 370. Further, the dequantized and inverse transformed coefficients are added to the prediction block through the adder 375, and a reconstructed block is generated.

The restoration block passes through the 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) can do. At this time, the filter unit 380 may be an adaptive in-loop filter. In addition, the deblocking filter can remove block distortion occurring at the boundary between the blocks. The SAO may add a proper offset value to the pixel value to compensate for coding errors. ALF can perform filtering based on the comparison between the reconstructed image and the original image. The reconstruction block having passed through the filter unit 380 can be stored in the reference picture buffer 390. [

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

4, an image decoding apparatus 400 to which a 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, 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 fast rate-distortion optimization-based quantization method is applied receives the bitstream output by the encoder that is a fast rate-distortion optimization-based quantization apparatus and decodes the bitstream into an intra mode or an inter mode, That is, a restored image. Specifically, in the intra mode, the switch is switched to the intra mode, and in the inter mode, the switch can be switched to the inter mode.

Also, the image decoding apparatus 400 to which the fast rate-distortion optimization-based quantization method is applied can obtain a reconstructed residual block from the input bitstream and generate a prediction block. Next, the image decoding apparatus 400 to which the fast rate-distortion optimization-based quantization method is applied may add a restored residual block and a prediction block to generate a reconstructed block.

The entropy decoding unit 410 may entropy-decode the input bitstream according to a probability distribution to generate symbols including a quantized level type symbol. In addition, the entropy decoding method may be performed in 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. The reconstructed residual block can be generated as a result of inverse quantization and inverse transform of the quantized level. At this time, the inverse quantization unit 420 can apply the quantization matrix to the quantized levels.

In the intra mode, the intraprediction unit 440 can generate a prediction block by performing spatial prediction using the pixel value of the already coded block around 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 restored residual block and the prediction block are added by the adder 455, and the added block can pass through the filter unit 460. [ The filter unit 460 may apply at least one of a deblocking filter, SAO, and ALF to a restoration block or a restored picture. The filter unit 460 can 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.

FIG. 5 schematically shows a divided structure of an image when encoding and decoding an image according to a fast rate-distortion optimization-based quantization method according to an embodiment.

The fast rate-distortion optimization-based quantization apparatus can perform coding and decoding with a coding unit (CU) in order to efficiently divide an image. A unit may consist of a combination of syntax elements and blocks containing image samples. Further, the unit may be divided, and at this time, the block corresponding to the unit may be divided in some cases.

5, in the HEVC, the fast rate-distortion optimization-based quantization apparatus may divide the image 500 sequentially in units of a maximum coding unit (LCU), and then determine a division structure in units of LCUs . Optionally, the partition structure may represent a distribution of an encoding unit (CU) for efficiently encoding an image within the LCU 510.

At this time, the fast rate-distortion optimization-based quantization apparatus can determine whether the distribution of the CUs is divided into four CUs, which are reduced to one half of the width and height of one CU. Of course, a partitioned CU may be recursively partitioned into four CUs whose halftone and vertical sizes are reduced by half, for a partitioned CU in the same manner.

In addition, the fast rate-distortion optimization-based quantization apparatus can recursively divide the partition of the CU to a predetermined depth. At this time, the depth information is information indicating the size of the CU and can be stored for each CU.

For example, the depth of the LCU may be zero, and the depth of the Smallest Coding Unit (SCU) may be a predefined maximum depth. Of course, the LCU is an encoding unit having the maximum encoding unit size as described above, and the SCU (Smallest Coding Unit) can be the encoding unit having the minimum encoding unit size.

The depth of the CU increases by one each time the LCU 510 divides into halves and half the size. In addition, each CU having a size of 2Nx2N has a size of 2Nx2N, and a CU that performs a division can be divided into 4 CUs having a size of 2Nx2N and a size of NxN. At this time, the size of N can be reduced to half each time the depth is increased by one.

Also, for example, the size of an LCU with a minimum depth of 0 is 64x64 pixels, and the size of an SCU with a maximum depth of 3 may be 8x8 pixels. At this time, the CU (LCU) of 64x64 pixels may be represented by depth 0, the CU of 32x32 pixels may be represented by depth 1, the CU of 16x16 pixel may be represented by depth 2, and the CU (SCU) of 8x8 pixel may be represented by depth 3.

In addition, information on whether to divide a specific CU can be expressed through division information of 1 bit for each CU. This division information may be included in all CUs other than the SCU. In some cases, if the fast rate-distortion optimization based quantization apparatus does not divide the CU, it may store 0 in the division information, and the fast rate- If the device divides the CU, it may store 1 in the partition information.

FIG. 6 illustrates a prediction unit type that an encoding unit to which a fast rate-distortion optimization-based quantization method according to an embodiment of the present invention is applied.

Referring to FIG. 6, the type of the prediction unit (PU) that the encoding unit (CU) can include is known. The fast rate-distortion optimization-based quantization apparatus can divide a CU that is no longer divided among the divided CUs 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 fast rate-distortion optimization-based quantization device as either a skip mode, an inter mode, or an intra mode, Depending on the mode, it may be partitioned into various forms. For example, in the case of skip mode, the fast rate-distortion optimization based quantization apparatus can support 2Nx2N mode 410 having the same size as the CU, without partitioning within the CU.

Also, in the case of the inter mode, the fast rate-distortion optimization based quantization device can support 8 partitioned forms in the CU. For example, the fast rate-distortion optimization based quantization apparatus may include a 2Nx2N mode 410, a 2NxN mode 415, an Nx2N mode 420, an NxN mode 425, a 2NxnU mode 430, a 2NxnD mode 435, nLx2N mode 440, and nRx2N mode 445, respectively. In addition, the fast rate-distortion optimization-based quantization apparatus can support the 2Nx2N mode 410 and the NxN mode 425 in the CU for the intra mode.

FIG. 7 illustrates a conversion unit included in an encoding unit to which a fast rate-distortion optimization-based quantization method according to an embodiment is applied.

Referring to Fig. 7, the type of the conversion unit (TU) that the encoding unit (CU) can include can be known. At this time, the conversion unit may be a basic unit used for conversion, quantization, inverse conversion, and dequantization in the CU. Also, the TU may be in the form of a square or a rectangle.

CUs that are not further partitioned among the CUs segmented from the LCU by the fast rate-distortion optimization-based quantization unit may be partitioned by a fast rate-distortion optimization based quantizer into one or more TUs. At this time, the partition structure of the TU may be a quad-tree structure. For example, in some cases, one CU 710 may be divided into one or more TUs according to a quad tree structure, and may be configured with TUs of various sizes.

FIG. 8 illustrates determining the optimal quantized level value among the fast rate-distortion optimization-based quantization methods according to one embodiment.

Referring to FIG. 8, the step of determining the optimal quantized level value among the fast rate-distortion optimization-based quantization methods performed by the fast rate-distortion optimization-based quantization apparatus is performed as follows.

In step S801, the image encoder, which is a fast rate-distortion optimization-based quantization device, can 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 the last one of the quantized levels having a non-zero value in the transform block. In addition, the last significant coefficient may be the first coefficient whose quantized level value is not 0 when the transform coefficient is quantized while scanning in the reverse diagonal scan order.

In step S801, the image encoder, which is a high-speed rate-distortion optimization-based quantization apparatus, calculates the quantized levels of the transform coefficients for the remaining transform coefficients excluding the last significant coefficient in the transform block, The rate-distortion cost can be calculated. At this time, the image encoder, which is a fast rate-distortion optimization-based quantization apparatus, determines the optimal quantized level based on the calculated rate-distortion cost. Of course, the remaining transform coefficients may be transform coefficients that occur after the last significant coefficient in the inverse diagonal scan order.

Specifically, in order to determine an optimum quantized level for one transform coefficient, the image encoder, which is a quantization apparatus based on a fast rate-distortion optimization, first determines whether a quantized level value for the transform coefficient is smaller than 3 do.

At this time, when the quantized level value for the transform coefficient is smaller than 3, the image encoder, which is a fast rate-distortion optimization-based quantization apparatus, substitutes the corresponding transform coefficient by Level, Level-1, and 0 The rate-distortion cost for each can be calculated. In this case, the Level value may be a quantized level value calculated in the step 2. When the quantized level value for the transform coefficient is greater than 3, the image encoder, which is a fast rate-distortion optimization-based quantization apparatus, replaces the corresponding transform coefficient with a level value and a value of 0, Cost can be calculated.

An image encoder, which is a fast rate-distortion optimization-based quantization device, can update the rate-distortion cost. At this time, the image encoder, which is a fast rate-distortion optimization-based quantization apparatus, can update the rate-distortion cost for encoding with 0 as a quantized level value for all transform coefficients in a transform block. In addition, an image encoder, which is a fast rate-distortion optimization-based quantization device, can update the 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, can update the rate-distortion cost for encoding a significant map of a transform block. At this time, the important map may be information on whether the 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, can update context information on the optimal quantized level for the transform coefficients. Also, the image encoder, which is a high-speed rate-distortion optimization-based quantization apparatus, can determine the quantized level having the smallest rate-distortion cost among the quantized levels as the optimum quantized level. At this time, the updated context information may be used in determining the quantized level for the next transform coefficient.

Also, in some cases, the image encoder, which is a fast rate-distortion optimization-based quantization device, may perform step S802 for the next non-zero transform coefficients in the transform block.

FIG. 9 illustrates a step of determining an optimal last significant coefficient among the fast rate-distortion optimization-based quantization methods according to an embodiment.

Referring to FIG. 9, the step of determining an optimal last significant coefficient among the fast rate-distortion optimization-based quantization methods performed by the fast rate-distortion optimization-based quantization apparatus may be performed as follows.

In step S901, the image encoder, which is a high-speed rate-distortion optimization-based quantization apparatus that is a high-speed rate-distortion optimization-based quantization apparatus, calculates a rate-distortion cost value d64BestCost as a rate- distortion cost value when a transform block is not coded Can be initialized.

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

Specifically, the image encoder, which is a high-speed rate-distortion optimization-based quantization apparatus, scans according to step S902 and regards a transform coefficient having a quantized level value larger than 1 as a last significant coefficient, and calculates a rate- You can calculate the totalCost.

Next, if the totalCost is smaller than d64BestCost, the image encoder, which is a fast rate-distortion optimization-based quantization apparatus, can set the position of the last significant coefficient to iBestLastIdxP1 indicating the optimal last position indicator and set d64BestCost to totalCost.

In this case, when calculating the rate-distortion cost, the rate-distortion cost can be calculated using Equation (3) below. ≪ EMI ID = 3.0 > D may be a mean square error of the difference values between the original transform coefficients and the reconstructed transform coefficients within the transform block, and R may be the bit rate using the associated context information.

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

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

The fast rate-distortion optimization-based quantization apparatus applies a scalar quantization scheme using a dead zone only to some transform coefficient positions or subblocks in a transform block, and performs rate-distortion optimization-based quantization according to the applied result, And the like.

Referring to FIG. 10, the transform block may have a size of 4x4, 8x8, 16x16, 32x32, etc. according to the transform size. At this time, the quantization apparatus based on the fast rate-distortion optimization can use an inverse diagonal scan, a horizontal scan, and a vertical scan in order to scan each transform block.

In addition, a transform block having a size of 8x8 or larger can be divided into 4x4 sub-blocks. 9 shows an 8x8 transform block having a 4x4 transform block and four 4x4 subblocks. In the 4x4 transform block, the transform coefficients or the quantized levels can be scanned in the reverse diagonal scan order as shown. Also, in the 8x8 transform block, the transform coefficient or the quantized level in the subblock can be scanned in the reverse diagonal scan order as shown, and the scan between the subblocks in the transform block can also be scanned in the reverse diagonal scan order have. At this time, the scan method in the conversion block and the sub-block and the scan between the sub-blocks are not limited to the inverse diagonal scan method, and the scanning methods such as reverse horizontal scan and reverse vertical scan mentioned above can also be used. Of course, the rate-distortion optimization-based quantization may be performed on a per transform block basis by a high-rate-distortion optimized quantization apparatus.

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

When the transformation is performed by the fast rate-distortion optimization-based quantization apparatus, the transformation coefficient occurs at the upper left side in the transformation block, and the transformation coefficient generated at the upper left after transformation may be relatively low frequency. In addition, the conversion coefficient generated in 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 plays a more important role than the high frequency component in the image encoder and decoder, which is a fast rate-distortion optimization based quantization apparatus.

Referring to FIG. 11, a total of 16 transform coefficients may exist in the 4x4 transform block. In total, 64 transform coefficients are included in the 8x8 transform block, 256 transform coefficients are totaled in the 16x16 transform block, and 1024 transform coefficients Lt; / RTI >

At this time, when the rate-distortion optimization-based quantization method performed by the fast rate-distortion optimization-based quantization apparatus is used, the optimal quantized level and the optimum last significant coefficient should be determined for a maximum of 1024 transform coefficients for a 32x32 transform block . Of course, in some cases, the complexity in the image encoder, which is a fast rate-distortion optimization-based quantization device, can be considerably increased, so that a fast rate-distortion optimization-based quantization device can prevent this.

The fast rate-distortion optimization-based quantization apparatus includes a transform coefficient block 1101 or a subblock 1102 in the transform block to reduce the number of optimal quantized levels and optimum final significant coefficients to be determined in the transform block. The optimal quantized level and the optimal last significant coefficient can be determined.

For example, the fast rate-distortion optimization-based quantization apparatus may be configured to optimize the quantized level and the final last significant coefficient only for the transform coefficients existing in some transform coefficients or some subblocks in the transform block depending on the size of the transform block You can decide. At this time, the fast rate-distortion optimization-based quantization apparatus can set the quantized level value to 0 for all the transform coefficient positions in the transform block or the transform coefficients not corresponding to some subblocks.

Also, as the case may be, the fast rate-distortion optimization based quantization apparatus can determine the optimal quantized level and the last significant significant coefficient only for some transform coefficient positions 1101 for the 8x8 transform block. Also, as the case may be, the fast rate-distortion optimization based quantization apparatus can determine the optimal quantized level and the last significant significant coefficient only for some sub-blocks 1102 for a 16x16 transform block.

Of course, the fast rate-distortion optimization based quantization apparatus can determine the optimum quantized level and the last significant coefficient only in the 4x4 transform block and the 32x32 transform block only for the transform coefficients existing in some transform coefficient positions or in some subblocks have. In addition, the fast rate-distortion optimization based quantization apparatus is capable of performing quantization on two 8x8, 16x16, or 32x32 transform blocks, respectively, from the last scan order according to reverse-scan, reverse-scan, 9, and 35 subblocks in the above-described manner.

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

According to one embodiment, the fast rate-distortion optimization-based quantization apparatus is capable of performing quantization based on an optimum intra-picture prediction mode only for a transform coefficient existing in some transform coefficient positions in a transform block or a partial transform block in accordance with an intra- The quantized level and the optimal last significant coefficient can be determined. The quantization apparatus based on the fast rate-distortion optimization is a mode in which, when the intra prediction mode is a mode having a direction similar to the vertical mode or the vertical mode, only the optimal quantized level and the optimal quantization level The last significant coefficient can be determined. In addition, when the intra prediction mode is a mode having a direction similar to that of the horizontal mode or the horizontal mode, the quantization apparatus based on the fast rate-distortion optimization has an optimal quantized level only for the two subblocks on the left in the 8x8 transform block And an optimal last significant coefficient can be determined.

According to one embodiment, the fast rate-distortion optimization-based quantization apparatus is configured to optimize quantization of only a transform coefficient existing in a partial transform block or a partial transform block in a transform block according to a prediction mode of an encoding unit including the transform block The level and the last significant coefficient of the optimum.

In addition, when the prediction mode is the intra-picture prediction mode, a large number of transform coefficients may exist in the low-frequency region relatively to the inter-picture prediction mode. In this case, the quantization apparatus based on the fast rate-distortion optimization may perform the above-described method on more transform coefficient positions or subblocks in the low frequency region than the inter-view prediction mode in the intra prediction mode.

According to one embodiment, a high-speed rate-distortion optimization-based quantization apparatus is a quantization apparatus for quantizing an optimal quantized level and a quantization level for only a transform coefficient existing in a partial transform block or a partial transform block in a transform block according to a luminance component or a chrominance component, It is possible to determine an optimum last significant coefficient. Since the luminance component is more likely to have a transform coefficient than the chrominance component, the high-speed rate-distortion optimization-based quantization apparatus is capable of performing the transform process on the luminance component with respect to the transform coefficient position or subblocks The above method can be performed.

In addition, the fast rate-distortion optimization based quantization apparatus does not apply the rate-distortion optimization-based quantization to the entire transform block as in the existing rate-distortion optimization-based quantization method. The fast rate-distortion optimization based quantization apparatus applies a scalar quantization method using a dead zone based on a relatively low frequency component, which is a relatively important component in a transform block, and then performs a rate-distortion optimization when a quantized level value for a low- Based quantization. In addition, if the quantized level value for the low-frequency component does not exist, the fast rate-distortion-based quantization apparatus can determine the quantized level value for the entire transform coefficient in the corresponding transform block to be zero.

At this time, the fast rate-distortion optimization-based quantization apparatus applies a scalar quantization scheme using dead zones only to some transform coefficient positions or subblocks in a transform block, and when a quantized level value exists in the corresponding transform coefficient position or subblock Quantization based on rate-distortion optimization can be applied. In addition, if the quantization level value of the corresponding transform coefficient position or the subblock does not exist, the quantization level value of the whole transform coefficients in the transform block may be set to zero.

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

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

Also, according to one embodiment, the transform coefficient position 1201 or subblock 1202 to which the scalar quantization method using the dead zone is to be applied may be determined according to a luminance component or a chrominance component which is a color component. Since the luminance component is more likely to have a transform coefficient than the chrominance component, the high-speed rate-distortion optimization-based quantization apparatus is able to obtain more transform coefficient positions or subblocks in the transform block for the luminance component than the transform block for the chrominance component It may be determined that the scalar quantization scheme using the dead zone is to be applied.

The apparatus described above may be implemented as a hardware component, a software component, and / or a combination of hardware components and software components. For example, the apparatus and components described in the embodiments may be implemented within a computer system, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA) A programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For ease of understanding, the processing apparatus may be described as being used singly, but those skilled in the art will recognize that the processing apparatus may have a plurality of processing elements and / As shown in FIG. For example, the processing unit may comprise a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as a parallel processor.

The software may include a computer program, code, instructions, or a combination of one or more of the foregoing, and may be configured to configure the processing device to operate as desired or to process it collectively or collectively Device can be commanded. The software and / or data may be in the form of any type of machine, component, physical device, virtual equipment, computer storage media, or device , Or may be permanently or temporarily embodied in a transmitted signal wave. The software may be distributed over a networked computer system and stored or executed in a distributed manner. The software and data may be stored on one or more computer readable recording media.

The method according to an embodiment may be implemented in the form of a program command that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions to be recorded on the medium may be those specially designed and configured for the embodiments or may be available to those skilled in the art of computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape; optical media such as CD-ROMs and DVDs; magnetic media such as floppy disks; Magneto-optical media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine language code such as those produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. 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.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. For example, it is to be understood that the techniques described may be performed in a different order than the described methods, and / or that components of the described systems, structures, devices, circuits, Lt; / RTI > or equivalents, even if it is replaced or replaced. Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (1)

A fast rate-distortion optimization-based quantization method performed by a fast rate-distortion optimization-based quantization apparatus,
Initializing the value of the first context information in the transform block to 1 and initializing the value of the second context information in the transform block to 0 to initialize the context information;
Scanning the transform coefficients in the transform block and quantizing the transform coefficients;
Calculating a quantized level value of the transform coefficients, determining a position of some transform coefficients in the transform block or an optimal quantized level value for a subblock in the transform block; And
Determining a position of the transform coefficient or an optimal last significant coefficient for the subblock
Lt; / RTI >
Further comprising applying scalar quantization using the dead zone to the position of the transform coefficient or the subblock,
Fast rate - distortion optimization based quantization method.

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