WO2017142360A1

WO2017142360A1 - Image encoding and decoding method, and image encoder and image decoder using same

Info

Publication number: WO2017142360A1
Application number: PCT/KR2017/001800
Authority: WO
Inventors: 권오진; 최승철
Original assignee: 세종대학교산학협력단
Priority date: 2016-02-19
Filing date: 2017-02-17
Publication date: 2017-08-24

Abstract

Disclosed is an image encoder comprising: a basic layer processor for converting a first dynamic range image into a second dynamic range image, and encoding the second dynamic range image so as to generate a basic layer code stream; an inverse quantizer for inversely quantizing the second dynamic range image quantized by the basic layer processor, and deriving DCT domain data; and an enhancement layer processor for deriving DCT domain data for the first dynamic range image, and deriving a first dynamic range image-related prediction coefficient from DCT domain data for the second dynamic range image and the DCT domain data for the first dynamic range image. According to the present invention, encoding and decoding, using the correlation of the first dynamic range image data and the second dynamic image data, of an HDR image having JPEG backward-compatibility can be provided such that encoding and decoding performance can be improved.

Description

【Specification】

[Name of invention]

Image encoding and decoding method, image encoder and image decoder using the same

Technical Field

The present invention relates to an image encoding and decoding technique, and more particularly,

The present invention relates to a method of encoding and decoding HDR High Dynamic Range (JPEG) backward compatible, and to an image encoder and an image decoder using the same.

Background technology

An image can generally be represented by a limited number of bits that represent a limited range of values to represent a luminance signal. The most common digital image formats currently in use use 24-bit (so-called 24-bit format) to store color and luminance information at each pixel in the image. For example, each value of red, green, and blue (Red, Green, and Blue) for a pixel may be stored in a range of 1 byte (8 bits). These images are called low dynamic range (LDR) images.

The brightness of light that can be detected by humans has a certain range. The ratio of the darkest and the brightest light that can be detected is called the dynamic range. In the ^10-3 dynamic range of the brightness (luminance) that is easy for a person to recognize ^{10 cd / m 2 (candel a} / m ') , while the conventional RGB color to use conventional expression by eight bits per

Alternative Paper (Article 126 of the Rules) The dynamic range of the digital camera / display is only limited to approximately 10 ² cd / m ² . Fortunately, in the camera industry, in LDR images, high dynamic range ('HDR') images representing each RGB color at high-bit-depth, such as 12-bit or 16-bit, etc. Conversion is beginning.

Displaying HDR images also requires a high-bit-depth output device. However, most existing output devices are still in LDR, and this imbalance between input and output devices in image mounts is expected to last for years. As a solution for visualizing HDR images on existing displays, a method using a tone-mapping operator (TMO) has been proposed that converts HDR images into LDR images.

On the other hand, in image coding, the legacy-JPEG (Legacy-JPEG) standard (ISO / IEC 10918) still dominates the photography market. However, this standard does not support HDR images. Although advanced image coding standards such as JPEG 2000 (IS0 / IEC 15444) or JPEG XR (ISO / IEC 29199) provide HDR image support, the adoption of HDR image coding by these standards is expected to be positive on the market. It is not becoming.

The JPEG Commission (SC29WG1) recognizes that the main cause of this phenomenon is due to lack of backward compatibility with L-JPEG, which is already a tool chain in the market, a new image encoding called JPEG XT (ISO / IEC 18477). Standardization work has been initiated. Three profiles called profiles A, B and C have been proposed for JPEG XT.

Alternative Paper (Article 126 of the Rules) There is a difference between these profiles in the method for generating and encoding the residual image.

As can be seen, the existing JPEG XT system provides backward compatible HDR image encoding, but has not shown satisfactory performance. [Detailed Description of the Invention]

[Technical problem]

An object of the present invention for solving the above problems is to provide an image encoding method compatible with backward JPEG.

Another object of the present invention for solving the above problems is to provide an image decoding method compatible with JPEG backwards.

Another object of the present invention for solving the above problems is to provide an image encoder that is backward compatible JPEG.

Another object of the present invention for solving the above problems is to provide an image decoder compatible with JPEG reverse direction.

Technical Solution

An image encoding method according to an embodiment of the present invention for achieving the above object, the step of converting a first dynamic range image to a second dynamic range image, and encoding a second dynamic range image to generate a base layer code stream Deriving DC screte cosine transform (DCT) domain data for the second dynamic range image, deriving DCT domain data for the first dynamic range image, DCT for the second dynamic range image DCT diagram replacement paper for domain data and the first dynamic range image (rule 126) Deriving prediction coefficients related to the first dynamic range image from the main data, and using the DCT domain data for the second dynamic range image and the prediction coefficients related to the first dynamic range image, the prediction DCT for the first dynamic range image. Deriving domain data.

Here, the first dynamic range image may be an HDR image, and the second dynamic range image may be an LDR image.

Deriving the prediction coefficients related to the first dynamic range image may include using the correlation of the DCT domain data for the first dynamic range image with respect to the DCT domain data for the second dynamic range image. The method may include calculating image related prediction coefficients.

The image encoding method uses at least one residual coefficient by using the DCT domain data for the second dynamic range image and the predictive DCT domain data for the dynamic range image derived from the prediction coefficients related to the thrust dynamic range image. The method may further include generating and generating a residual layer codestream including the first dynamic range image related prediction coefficient and the at least one residual coefficient.

The generating of the base layer codestream may include converting the first layer dynamic range image into the second dynamic range image by performing a tone-mapping operation on the first dynamic range image.

Generating the base layer codestream also includes color transforming the second dynamic range image, DCT transforming the color transformed image, quantizing the DCT transformed image, and entropy the quantized image. Alternative paper for encoding steps (Article 126) It may include.

In this case, the image quality coefficient used in the quantization of the DCT transformed image may be the same as the image quality coefficient used for quantization of the residual coefficient.

Additionally, deriving DCT domain data for the second dynamic range image may include performing inverse quantization on the quantized DCT transformed image.

In this case, the AC coefficient of the DCT domain data for the first dynamic range image and the AC coefficient of the DCT domain data for the second dynamic range image are expressed as a function such as a polynomial, an exponential function, a logarithmic function, and a trigonometric function. It can have a correlation. In addition, the DC coefficient of the DCT domain data for the first dynamic range image has a correlation represented by a prediction curve including a plurality of intervals with respect to the DC coefficient of the DCT domain data for the second dynamic range image, Each interval of the prediction curve may be defined by the same or different functions such as polynomials, exponential functions, logarithmic functions, trigonometric functions, and the like. In accordance with another aspect of the present invention, there is provided a method of decoding an image, the method comprising: receiving a residual layer codestream including a first dynamic range image related prediction coefficient, receiving a base layer codestream, Decoding the received base layer code stream to generate a second dynamic range image; deriving DCT domain data for the second dynamic range image; deriving a residual DCT domain data; Image-related prediction coefficients and the second dynamic range already substituted (Article 126) Calculating predictive DCT domain data for the first dynamic range image from the DCT domain data for the digital image; adding the residual DCT domain data and the predictive DCT domain data for the first dynamic range image to add the DCT for the first dynamic range image. Reconstructing domain data, and decoding the DCT domain data for the first dynamic range image to generate a first dynamic range image.

Computing the predictive DCT domain data for the first dynamic range image may include deriving a first dynamic range image related prediction coefficient from the residual codestream and the DCT domain data for the second dynamic range image. 1 may include applying a function by the prediction coefficients related to the dynamic range image. In accordance with another aspect of the present invention, there is provided a method of decoding an image, the method comprising: receiving a residual layer codestream including a first dynamic range image related prediction coefficient, receiving a base layer codestream, Deriving spatial domain data for a low 12 dynamic range image by performing an inverse -DCT lnverse Discrete Cosine Transform on the received base layer code stream, wherein the first dynamic range image 련: series prediction coefficients and the first Calculating predictive spatial domain data for the first dynamic range image from spatial domain data for the dynamic range image, performing inverse-DCT transformation on the residual signal included in the residual layer codestream, and The spatial prediction data related to the first dynamic range image and the inverse-DCT transformed residual scene replacement paper (Rule 12) Article 6) Reconstructing the first dynamic range image from the call.

The step of calculating the predictive spatial domain data for the first dynamic range image may include deriving a first dynamic range image related prediction coefficient from the residual codestream and storing the predicted spatial domain data for the second dynamic range image. 1 1 may include applying a function by the prediction coefficients related to the dynamic range image. In accordance with another aspect of the present invention, an image encoder converts a first dynamic range image into a second dynamic range image and encodes a second dynamic range image to generate a base layer codestream. A base layer processor to generate, an inverse quantizer for deriving DCT domain data by performing inverse quantization on the second dynamic range image quantized by the base layer processor, and deriving DCT domain data for the first dynamic range image And an enhancement layer processor for deriving a prediction coefficient related to the first dynamic range image from the DCT domain data for the second dynamic range image and the DCT domain data for the first dynamic range image. Here, the first dynamic range image may be an HDR image, and the second dynamic range image may be an LDR image.

The enhancement layer processor is configured to utilize the correlation of the DCT domain data for the first dynamic range image with respect to the DCT domain data for the second dynamic range image to predict the first dynamic range image and the first dynamic range. Example Alternative Paper for Images (Article 126) It may include a predictor for calculating side DCT domain data.

The enhancement layer processor may also generate at least one residual coefficient using the DCT domain data for the second dynamic range image and the prediction DCT domain data for the first dynamic range image derived from the prediction coefficients related to the first dynamic range image. And a residual layer codestream including the first dynamic range image related prediction coefficient and the at least one residual coefficient.

Here, the image quality coefficient used for quantization of the second dynamic range image performed by the base layer processor may be the same as the image quality coefficient used for quantization of the residual coefficient performed by the enhancement layer processor.

The base layer processor may include a tone-mapping operator that performs a tone-mapping operation on a first dynamic range image and converts the second dynamic range image into a color. And a color converter for converting, a DCT converter for DCT converting the color converted image, a quantizer for quantizing the DCT converted image, and an entropy encoder for entropy encoding the quantized image. According to another aspect of the present invention, an image decoder according to another embodiment of the present invention receives a base layer codestream, decodes the base layer codestream, and decodes DCT domain data for a second dynamic range image. Receive a residual layer codestream including a base layer decoder for generating a second dynamic range image and prediction coefficients related to the first dynamic range image, and receiving prediction coefficients related to the first dynamic range image and the second dynamic range image From the DCT domain data for the first dynamic replacement sheet (Rule 126) The DCT domain data of the range image may be calculated, and the enhancement layer decoder may reconstruct the first dynamic range image by performing inverse-DCT transformation on the DCT domain data of the first dynamic range image. According to still another aspect of the present invention, an image decoder according to another embodiment of the present invention receives a base layer codestream and performs entropy decoding, inverse-quantization, and inverse-DCT transform on the base layer codestream. Receiving a residual layer codestream including a base layer decoder and a first dynamic range image-related prediction coefficient and a residual signal to derive a second dynamic range image, and performing the first dynamic range image-related prediction coefficient and the second Calculating spatial prediction data related to the first dynamic range image from spatial domain data for a dynamic range image, and reconstructing a first dynamic range image from the first dynamic range image related spatial prediction data and an inverse-DCT transformed residual signal It may include an enhancement layer decoder.

【Effects of the Invention】

According to the HDR image encoding method and decoding method of the present invention as described above, JPEG backward compatible HDR image encoding and decoding that utilizes the correlation of the HDR image to the tone-mapped LDR image in the DCT domain can be provided. It can improve the encoding and decoding performance.

[Brief Description of Drawings]

1 is a block diagram of a JPEG XT encoding system.

2 is a block diagram of a block diagram of an HDR image encoder according to an embodiment of the present invention (rule 126) All.

3 is a block diagram of an HDR image decoder according to an embodiment of the present invention.

4 illustrates a plurality of image samples for explaining an experimental result of the HDR image encoding method and the decoding method according to an embodiment of the present invention.

5 is an exemplary diagram illustrating a distribution of AC coefficients for various TM0s according to an embodiment of the present invention.

6 is an exemplary diagram illustrating a distribution of DC coefficients for various TM0s according to an embodiment of the present invention.

7 is a graph illustrating a concept of deriving a predictive HDR DC value according to another embodiment of the present invention.

8 is a block diagram of an HDR image decoder according to another embodiment of the present invention.

9 is a flowchart illustrating an encoding method according to an embodiment of the present invention.

10 is an operation flowchart of a decoding method according to an embodiment of the present invention.

11 is an operational flowchart of a decoding method according to another embodiment of the present invention. [Best form for implementation of the invention]

As the invention allows for various changes and numerous embodiments, particular embodiments will be illustrated in the drawings and described in detail in the written description. However, this is not intended to limit the present invention to specific embodiments, and includes all changes, equivalents, and substitutes included in the spirit and technical scope of the present invention. Should be understood. In describing the drawings, similar reference numerals are used for similar elements.

Terms such as first, second, A, and B may be used to describe various components, but the components should not be limited by the terms. The terms are used only for the purpose of distinguishing one component from another. For example, the first component may be referred to as a second component without departing from the scope of the present invention, and similarly, the second component may also be referred to as the first component. The terms and / or include any combination of a plurality of related items or a plurality of related items.

When a component is said to be "connected" or "connected" to another component, it may be directly connected or connected to that other component, but other components may be present in the middle. It should be understood. On the other hand, when a component is said to be "directly connected" or "directly connected" to another component, it should be understood that no other component exists in the middle.

The terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting of the invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this application, the terms "comprise" or "have" are intended to indicate that there is a feature, number, step, action, component, part, or combination thereof described in the specification, one or more other The presence or substitution of features, numbers, steps, actions, components, parts or combinations thereof (Rule 126) It should be understood that the possibility of addition is not excluded in advance.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art. Terms such as those defined in the commonly used dictionaries should be construed as having meanings consistent with the meanings in the context of the related art, and, unless expressly defined in the present application, in ideal or overly formal meanings. Not interpreted. Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. 1 is a block diagram of a JPEG XT encoding system.

The JPEG XT encoding system shown in FIG. 1 can be applied for profiles A, B and C.

As shown in FIG. 1, the JPEG XT encoding system includes a tone-mapping operator 10, an inverse -TMO (ll), a residual image generator 40, in addition to a legacy-JPEG encoder 20 and a legacy-JPEG decoder 30. And residual image encoder 50.

The JPEG XT encoding system including these detailed configurations outputs data of two layers, a base layer codestream and an enhancement layer, that is, a residual-layer codestream.

HDR images input to the JPEG XT encoding system are converted to tone-mapped LDR images by the tonemapping operator (10), color converters, DCT converters, and alternative paper (Article 126). Composes a base layer codestream that is compressed by a legacy -JPEG encoder, including a quantizer and an entropy encoder, and provides legacy -JPEG backward compatibility.

The enhancement layer (i.e., residual layer) codestream is a signal whose HDR image is output via the tonemapping operator (10), legacy -JPEG encoder (20), legacy -JPEG decoder (30), and inverse -TMO (ll). And a residual image generator 40 and a residual image encoder 50 that generate the residual image by using the HDR signal as an input.

At this time, in the legacy -JPEG encoder 20, the residual image encoder 50, and the quantizer, two image quality coefficients q and Q are used, respectively. Also, the choice of TM0 is given to the user, so any TM0 can be used with JPEG XT. Conversely, the TMO (ll) information may be included in the residual layer codestream used when reconstructing the HDR version of the residual layer decoder → LDR codestream. 2 is a block diagram of an HDR image encoder according to an embodiment of the present invention.

An image encoder according to an embodiment of the present invention includes a tone mapping operator 100 and a legacy -JPEG encoder 200. Tone mapping operator 100 and legacy-JPEG encoder 200 may be referred to herein as a base layer processor.

The image encoder according to an embodiment of the present invention also does not include a legacy -JPEG decoder, unlike the JPEG XT encoder described with reference to FIG. 1, but instead includes a scaler 301, a color converter 310, a DCT converter 320. , Enhancement layer processor 300 and quantizer 330, entropy encoder 340, and HDR predictor 350, and de-quantization replacement sheet (Rule 126) And group 331.

An image encoder according to an embodiment of the present invention converts a first dynamic range image into a second dynamic range image, and encodes a second dynamic range image to generate a base layer code stream, wherein the base layer processor Inverse quantizer deriving DCT domain data by performing inverse quantization on the second dynamic range image quantized by D, and deriving DCT domain data for the first dynamic range image, and DCT for the second dynamic range image. And an enhancement layer processor for deriving a first dynamic range image related prediction coefficient from domain data and DCT domain data for the first dynamic range image.

Here, the first dynamic range image may be represented using a larger amount of data than the second dynamic range image, the first dynamic range image may be an HDR image, and the second dynamic range image may be an LDR image.

Here, the enhancement layer processor 300 is a predictor that calculates prediction coefficients related to the U dynamic range image by utilizing the correlation of the DCT domain data for the first dynamic range image with respect to the DCT domain data for the second dynamic range image. The enhancement layer processor may also generate at least one residual coefficient using the DCT domain data for the first dynamic range image and the first dynamic range image related prediction coefficients, and the first dynamic range. A residual layer codestream including the image related prediction coefficients and the at least one residual coefficient is generated. Alternative paper according to the present invention through an embodiment of the image encoder as shown in Figure 2 (rule 126) Other JPEG backward-compatible HDR image coding may be implemented.

In FIG. 2, base layer encoding is applied in the same manner as the existing profile. That is, the HDR image input to the image encoder according to the present invention is tone-mapped by the tone mapping operator (TM0) 100 to be converted into an LDR image, a color converter 210, a DCT converter 220, a quantizer 230, constructs a base layer codestream compressed by legacy-JPEG encoder 200 including entropy encoder 240 and providing legacy-JPEG backward compatibility.

Here, the tone mapping operator (100) may be referred to as dynamic range compression, and converts an image HDR image into an 8-bit LDR image by tone mapping an image HDR image without losing the features and details such as edge information from the original image. .

The color converter 210 converts the LDR image represented by RGB (Red-Green-Blue) to YCbCr. DCT converter 220 performs an 8x8 block-based DCT transform on the image data represented by YCbCr. Here, DCT is one of techniques widely used for frequency conversion of an image and converts image data in the spatial domain into image data in the frequency domain using a cosine basis. When the DCT conversion is performed, the resulting DC and AC components, i.e., the DC coefficient and the AC coefficient are obtained.

The quantizer 230 receives the transform coefficient changed in the frequency domain by the DCT 220 as an input value and maps it to a discrete value. Data loss occurs during the quantization process, and continuous or large amounts of input data are mapped to a few discrete symbols after quantization. In addition, entropy encoder 240 receives the output of quantizer 230 and performs entropy encoding. Here, entropy encoding alternative paper (Article 126) Is lossless compression, and is a process of minimizing the amount of data necessary for representation by variably allocating the length of a symbol according to the occurrence probability of the symbol.

Meanwhile, the residual idling coding according to the exemplary embodiment of the present invention illustrated in FIG. 2 is clearly distinguished from the residual layer coding illustrated in FIG. 1.

First, the input HDR image is input to the scaler 301. Scaler 301 scales the range of pixel values of the input HDR image to the LDR image range, where scaling is a uniform and reversible f loat ing-point scaling operation. When the scaling operation is complete, the color representation of the LDR image is converted into a YCbCr representation by the color converter 310 and an 8x8 block-based DCT is performed by the DCT converter 320.

One of the main features of HDR image coding proposed by the present invention is to perform HDR prediction based on the DCT coefficients of the tone-mapped LDR image encoded in the base layer, and each DCT coefficient of the input HDR image, and to estimate prediction coefficients and residuals. It is a configuration to generate a hierarchical code stream. In this regard, the HDR predictor 350 shown in FIG. 2 plays this role.

The HDR predictor 350 outputs the output of the inverse quantizer 331, which inversely quantizes the data output by the quantizer 230 in the encoder 200, that is, the DCT of the ton-mapped LDR image. Receive a coefficient as input. HDR predictor 350 also receives the DCT coefficients of the input HDR image as another input to derive the predictive HDR DCT coefficients and the prediction coefficients. The difference between the DCT coefficients of the input HDR image and the predictive HDR DCT coefficients forms a residual DCT coefficient. The residual DCT coefficients are quantized by quantizer 330 and entropy coded by entropy encoder 340. Here, quantizer 330 replacement paper (rule 126) Is the same as the quantizer 230 in the encoder, entropy encoder 340 and also performs the same role as the entropy encoder 240 in the encoder.

Here, another point to note is that in the existing profile described with reference to FIG. 1, two image quality coefficients are used for base layer coding and residual layer coding, respectively, in the embodiment of the present invention shown in FIG. According to the HDR image coding system, the image quality coefficient q used in the base layer may be used in the same way for the residual layer.

The finally generated residual layer codestream is composed of prediction coefficients estimated by the HDR predictor 350 and entropy coded residual DCT coefficients.

Meanwhile, the DCT converter 220 of the base layer encoding process and the DCT converter 320 of the residual layer encoding process perform DCT conversion on the basis of blocks on the Y, Cb, and Cr color elements of the input image, and the resulting DCT coefficients. Are rearranged into one-dimensional vectors in zigzag order.

In FIG. 2, the k-th DCT coefficient in the first block of the input HDR image output by the DCT converter 320 is denoted by d, and the tone output by the de-quantizer 331.

The de-quantized DCT coefficients of the mapped LDR image are indicated by O). Also,

C ^ 0) and (0) is defined as defined and 'k is not 0, c' and _^ ^ '醒(Jc), the AC coefficients by the DC coefficient.

HDR predictor 350 is based on C ^ O) for each Y, Cb and Cr color element.

Alternative Paper (Article 126 of the Rules) Predict C ^ _{DR C.} For the prediction according to the present invention, DC and AC utilizes the correlation between e _'i lc and C' (with respect to both types of system can Below regard to the estimate of the prediction coefficients performed by the HDR predictor 350 It will be described in detail with reference to Figures 5 to 7. Figure 3 is a block diagram of an HDR image decoder according to an embodiment of the present invention.

3, HDR image decoding, which is a reverse operation of HDR image encoding, according to the present invention, can be described.

As shown in FIG. 3, the HDR image decoder of the present invention may include a base layer decoder 400 and an enhancement layer decoder 500 that process legacy-JPEG compatible base layer codestreams.

The base layer decoder 400 receives the base layer codestream, decodes the base layer codestream, derives DCT domain data for the second dynamic range image, and generates a second dynamic range image. The enhancement layer decoder 500 receives the residual layer codestream including the first dynamic range image related prediction coefficients, derives the first dynamic range image related prediction coefficients and the residual DCT domain data, and the first dynamic range image. in DCT-domain data for the relevant prediction coefficients and the shop second dynamic range image portion emitter said first calculates the predicted DCT domain data for the dynamic range image, the first dynamic range already and the predicted DCT domain data as to whether ^"residual DCT Sum domain data

Alternative Paper (Article 126 of the Rules) To reconstruct the DCT domain data for the first dynamic range image.

Enhancement layer decoder 500 includes an HDR predictor 550, an entropy decoder 540, an inverse-quantizer 530, an inverse color transformer 510, and an inverse-scaler 501 that process a residual layer codestream. It may include.

In FIG. 3, base layer decoding is performed by a legacy legacy-JPEG decoder 400, and a legacy-JPEG decoder 400 includes an entropy decoder 410, an inverse-quantizer 420, and an inverse DCT converter 430. , And inverse-color converter 440.

The basic trade-off codestream input to the image decoder of FIG. 3 is converted into a quantized stream through an entropy decoder 410 and c _DR j ^ transformed into a DCT domain via an inverse quantizer 420. . Data in the DCT domain is converted into an LDR image, which is finally expressed in RGB, via an inverse DCT converter 430 and an inverse-color converter 440.

On the other hand, the prediction coefficients included in the residual layer codestream are input to the HDR predictor 550, and the residual tradeoff codestream passes through an entropy decoder 540 and an inverse-quantizer 530, which is a DCT coefficient of the residual signal ^. Is converted to). In addition, the HDR predictor 550 receives as input input prediction coefficients included in ο ^ ₎ (Α−) and the residual layer codestream, and derives the predictive HDR DCT coefficients through HDR prediction.

The HDR predictor 550 according to an embodiment of the present invention located in the decoder uses a prediction coefficient included in the residual layer codestream to replace the encoder stage shown in FIG. 2 (rule 126). The HDR predictor 350 derives the same HDR DCT coefficients as the predicted HDR DCT coefficients.

The DCT coefficients of the residual signal and the predicted HDR DCT coefficients c ^ _M (A-) are summed to form f ^ in the form of a reconstructed HDR DCT coefficients, and the reconstructed HDR DCT coefficients are inverse2 "transformers (520), inverse- Finally, the image is finally restored to the HDR image through the color converter 510 and the inverse scaler 501. As described with reference to the embodiment of FIGS. From the point of view, there is a difference from the existing profile described in FIG.

First, existing profiles create their residual images in the spatial domain. Thus, conventionally, as shown in FIG. 1, a full L-JPEG decoding process is required in JPEG XT encoding.

In addition, conventional profiles A and B generate their residual images in the form of an image divided by an HDR original image at each pixel in tone-mapped LDR images, and profile C generates an HDR original image and a tone-mapped LDR image. Take the difference image as the residual image. However, the present invention generates residual data in the DCT domain. In addition, the L-JPEG decoding process is not required in the JPEG XT encoding according to the present invention, which means an effect of reducing the encoding time.

Secondly, the existing prop ^ uses two quality factors.

Alternative Paper (Article 126 of the Rules) One is used for the base layer and the other for the residual layer, denoted q and Q, respectively, as shown in FIG. Professional users will find the best combination of these two quality factors for efficient image coding, but this approach will be tricky for the average user. The present invention enhances user convenience by enabling the use of only one image quality coefficient that optimizes rate-distort ions to code the base and residual layers together. 4 illustrates a plurality of images for explaining an experimental result of the HDR image encoding method and the decoding method according to an embodiment of the present invention.

Three different HDR sample images as shown in (a) of FIG. 4 were used to confirm c _M 0t) and d 腿 ex correlations in the DC and AC coefficients according to the present invention.

Fig. 4 (a) shows the resultant image of uniformly quantizing the HDR sample image for display purposes and Fig. 5 (b) shows the tone-mapped LDR image using the TM0 technique proposed by Reinhard et al. .

Experiments on the HDR sample images of FIG. 4 show that the same conclusions are reached in the design of the HDR predictor according to the present invention even when the target image is changed.

In addition, in the present invention, five TM0s were selected from among several selectable TM0 techniques, and the correlation between 飄 and C ^^ Ar) in the DC coefficient and AC coefficient was examined.

Alternative Paper (Article 126 of the Rules) 5 is an exemplary diagram illustrating a distribution of AC coefficients for various TM0s according to an embodiment of the present invention.

The five TM0 techniques used in FIG. 5 are expressed as "Reinhard02", "Drago03", "iCAM06", "Mant iuk08" and "Mai ll". Also, the image quality factor q was preset to 70. In experiments with different image quality factors, the same distribution was observed. Therefore, the effects of other image quality factors in designing the HDR predictor were negligible.

More specifically, FIG. 5 shows the AC coefficient distribution for C ′ _IDR (k) of c ^ O) for various TM0 techniques. In each graph illustrated in FIG. 5, the horizontal axis means ^^ O), the vertical axis means _Γ '(), the Υ element is black, the Cb element is blue, and the Ci- element is represented by red.

It can be seen from FIG. 5 that the AC coefficient of c ^^ (A-) is closely correlated with the AC coefficient of i _ZDK (A-) for all Y, Cb and Cr color elements. More specifically, e ^! _If the horizontal axis represented by _w is X and the vertical axis represented by _y is _y , for example, if the "Reinhard02" TM0 technique is used for the "01" image, then y = 0.55x, for each color element, A relation that can be expressed as y = 0.28x and y = 0.42x is derived.

Also, if the image is changed or the TM0 technique applied is changed, the correlation between the AC coefficient of c ^^ O) and the ^AC coefficient of e ⁱ ', ie, the AC system of d 麗 and

Alternative Paper (Article 126 of the Rules) The number distribution also changes. For example, it can be seen that the "03" image shows a wider distribution than all other sample images for all TM0 techniques, and the iCAM06 TM0 technique shows a wider distribution than all other TM0 techniques for all other sample images. have.

However, Y, there is a close correlation between the AC coefficients of the AC coefficient _C ^ A :) and C ^l _{l R} (k-) for both Cb and Cr color component relationships and is clear, in one embodiment of the present invention The C ^ approximation can be defined with the c _DR c ^ linear polynomial function for the sample image and TM0 case. However, the correlation between the AC coefficients of the DCT domain data for the first dynamic range image and the AC coefficients of the DCT domain data for the second dynamic range image according to the present invention is not limited to the first-order polynomial function. For example, it may be expressed as a polynomial, an exponential function, a logarithmic function, a trigonometric function, and the like.

Therefore, in the present embodiment, the AC coefficient related prediction may be performed for each of the Y, Cb, and Cr color elements, which may be defined by Equation 1 below.

[Equation 1]

.,: Λ; „. ₁ ：

AC-^ G 'gyC ^ In Equation 1, _aAC may mean a coefficient that minimizes a mean square error (MSE) between C ^^ O) and C ^ A.

Alternative Paper (Article 126 of the Rules) 6 is an exemplary diagram illustrating a distribution of DC coefficients for various TM0s according to an embodiment of the present invention.

As in FIG. 5, the five TM0 techniques used in FIG. 6 are “Reinhard02”,

"Drago03", "iCAM06", "Mantiuk08" and "Mai 11". Also, the image quality factor q was also set to 70 in advance.

6 shows the DC distribution of e and (^ (0) for various TM0;

And C ^^ O), the DC distribution of the examined eu) and C ^ e through ⁵

It can be seen that it is quite different from the AC coefficient distribution.

In FIG. 6, the DC coefficient of the image reflects the averaged pixel value in units of blocks and TM0 serves to improve the dynamic range of luminance.

The distribution can be interpreted as a global aspect of the reverse behavior of TM0 adopted for each image. Although the distributions of Y, Cb and Cr for _C ^ _{M (0)} vs C ^ O) are different, they show a very high correlation of C ^ O) for c ^ _{A (0} ).

Therefore, according to an embodiment of the present invention, ce (O) for each color element of Y, Cb, and Cr is predicted by a cubic equation function of C ′ _IDR (0) defined by Equation 2 below.

[Equation 2]

Alternative Paper (Article 126 of the Rules) In Equation _{2, a) C, b,} c and d may refer to coefficients that minimize the mean square error (MSE, mean square error) between _c _'lmA and e ^). That is, HDR prediction according to an embodiment of the present invention may be performed by using a least square method.

The prediction coefficients, which consist of a total of 15 real values that can be defined as five constants of the symbols _{a j3C} , ό, c, and ^ for each of the three elements Y, Cb, and Cr, can be additionally included in the residual layer code stream. have. FIG. 7 is a graph illustrating a concept of dividing a predicted HDR DC value into intervals according to another embodiment of the present invention.

In the embodiment of FIG. 7, the X axis is an LDR DC coefficient value, the y axis is a predictive HDR DC coefficient value, and a range of values of the LDR DC coefficient is —1024 to 1023.

6, the coefficients ^, b, c, and d of Equation 2 can be obtained using the least square method. From the point on the prediction curve defined by these coefficients, find the point (pl, p2) whose vertical distance from the point on the starting line and the end point of the prediction curve is the maximum in the positive and negative directions. Can be set as the reference point for dividing the section. If the cubic equation and the straight line do not meet, pl and p2 can be arbitrarily designated as -200, 200. However, it does not limit pl, p2 to -200 and 200.

Referring to the graph of FIG. 7, the curve defined by the cubic equation is divided into three sections based on pl and p2, and the optimum prediction curve coefficients are extracted for each section.

Alternative Paper (Article 126 of the Rules) can do. Defined according to one embodiment shown in FIG.

One equation may be defined as Equation 3 below.

[Equation 3]

In Equation 3, _α ,, bi _{(C l} , ^ are the coefficients for interval 1 (-1024 to pi) and ^a DCl are the coefficients for interval 2 (pi to p2), and in interval 3 (p2 to 1024) Coefficients.

However, the three intervals defined in Equation 3 are merely exemplary, and the prediction function according to the present invention may be divided into any N intervals, and each interval may have various forms, for example, a polynomial or an exponential function. . Can be defined as a logarithmic function, a prediction function having a form defined by a trigonometric function round FIG. 8 is a block diagram of an HDR image decoder according to another embodiment of the present invention.

FIG. 8 illustrates a decoder according to another embodiment different from the HDR image decoder according to the embodiment shown in FIG. 3, wherein the decoder shown in FIG. 8 spatially stores a residual layer codestream encoded using the HDR predictor in the DCT domain. Process on the domain. Therefore, the decoder according to the present embodiment replaces the residual data represented by the DCT domain with the spatial map (Article 26). Switch to main and perform HDR prediction in the spatial domain.

The HDR image decoder according to another embodiment of the present invention illustrated in FIG. 8 includes a decoder 400 for processing a legacy -JPEG compatible base layer codestream and a spatial domain predictor (for processing a residual layer codestream). 551, an enhancement layer decoder 500 including an entropy decoder 540, an inverse-quantizer 530, an inverse-color converter 510, and an inverse-scaler 501.

In the embodiment of FIG. 8, the base layer decoding is performed by the legacy legacy-JPEG decoder 400, and the legacy-JPEG decoder 400 includes an entropy decoder 410, an inverse-quantizer 420, and an inverse DCT converter ( 430, and an inverted-color converter 440. The base layer codestream input to the image decoder of FIG. 8 is converted into a quantized stream through an entropy decoder 410 and an _{IDR (J)} — transform represented by the DCT domain via an inverse quantizer 420. do. c (A is converted to _lDR (ik ᅳ) via an inverse DCT converter 430 and converted to an LDR image that is finally expressed in RGB via an inverse-color converter 440.

On the other hand, the prediction coefficients included in the residual layer codestream are input to the spatial domain predictor 551, and the residual layer codestream is passed through an entropy decoder 540 and an inverse-quantizer 530, which is a low DCT coefficient of the residual signal. And the DCT coefficients of the residual signal are transformed to via an inverse-DCT converter 521. _(w) is added to the output of the spatial domain predictor 551, input to the inverse-color converter 510, and finally reconstructed into an HDR image via the inverse-scaler 510. Alternative Paper (Article 126 of the Rules) More specifically, the spatial domain predictor 551 receives the prediction coefficients included in the residual layer codestream received from the encoder and the inverse-DCT transformed base layer data ' _lDR as input and performs HDR prediction.

In relation to this, the result of performing inverse-DCT (IDCT) transformation on the DCT coefficient C. ' _HDR (k) of the HDR image reconstructed in the DCT domain shown in FIG. 3 is the same as If the expression of the spatial domain of c ^ it) of the DCT domain can be expressed as Equation 4 below.

[Equation 4]

X ¹ HDR OO = IDCT {C ^! H _D R i)}

= IDCT {E ^r (k) + C ¹ HOR (k)}

= IDCT {E '(ik ^} + IDCT {C! 腿 ()} where f Ot) is the residual signal and the DCT coefficient carried over the residual layer stream. In order to derive ^^ () in Equation ⁴ , an inverse -DCT operation may be performed on e ⁱ _HD .

Meanwhile, the second and third terms of Equation 4 are performed by performing inverse -DCT transform on HDR ov ^, which is a signal predicted through the HDR predictor, and performing inverse -DCT transform. Expand to represent the value of the image. Therefore, the HDR image γ reconstructed in the spatial domain may be generated as the sum of the reconstructed residual image and the reconstructed predictive HDR image.

Alternative Paper (Article 126 of the Rules) Equation 4 is developed to reconstruct an HDR image in a spatial domain of a residual layer codestream encoded using an HDR predictor in a DCT domain.

First, _. ^^ ⁽⁰⁾ — can be summarized as in Equation 5 below.

[Equation 5]

_/Of

a DC

Returning to Equation 4, the equation o ^^ t) is decomposed into a DC component and an AC component as shown in Equation 6 below to calculate fflc ^^)}.

Alternative Site (Article 26) [Equation 6]

In Equation 6 nii means the i-th element of the 8x8 block containing m. Through the developed equation, it was confirmed that the decoding process in the existing DCT domain can be decoded using the pixel value of the spatial domain in which the inverse-DCT transform is performed. Through this, the decoding process in the spatial domain according to an embodiment of the present invention may be performed by the decoder shown in FIG. 8.

In summary, ^{1 ′} (input value) input to the inverse color converter 510 in FIG. 8 may be expressed by Equation 7 below.

Alternative Site (Article 26) [Equation 7]

X 麵 (쩨 () + 64 ∑ ^x ( ^ma AC ^{X LM} ( ^m

Where ^ (, «) represents the result of performing ^IDCT on the residual signal,

(° ¹ DC- _AC )

⁶⁴ is a value calculated by the spatial domain predictor 551 as a result of predicting the HDR value by using the reconstructed LDR value mass (,?). When the HDR prediction method in the spatial domain according to the present invention is summarized in a more comprehensive concept, it may be expressed by Equation 8 below. [Equation 8]

ϊ = 1 In one embodiment of the invention shown in FIG. 8, 1 is ("-^ AC) and 5 is

64, and a description and an embodiment of a decoding method in the spatial domain in the case of _^AC, in accordance with another embodiment of the values A and B of the present invention may be replaced with a different value.

9 is a flowchart illustrating an encoding method according to an embodiment of the present invention. The encoding method shown in Fig. 9, but may be performed by the encoder shown in Figure _2, it is not the operation subject is not limited thereto.

Alternative Site (Article 26) An encoding method according to an embodiment of the present invention first converts a first dynamic range image into a second dynamic range image (S910), and encodes a second dynamic range image to generate a basic layer code stream (S920). . Here, the first dynamic range image may be an HDR image, and the second dynamic range image may be an LDR image.

Although not shown in detail in FIG. 9, step S920 of generating a base layer codestream may be performed by performing a tone-mapping operation on a first dynamic range image to convert the second dynamic range image to a second dynamic range image. Color converting the range image, DCT converting the color converted image, quantizing the DCT converted image, and entropy encoding the quantized image.

Thereafter, DCT domain data for the second dynamic range image is derived (S930), and DCT domain data for the first dynamic range image is derived (S940). Deriving DCT domain data for the first dynamic range image (S940) includes scaling to the second dynamic range image data range, color converting the scaled image, and DCT converting the color converted image. It may include. Here, for convenience of description, steps S930 and S930 are described as being sequentially executed, but two steps may be performed simultaneously, step S930 may be executed first, and step S940 may be performed later. In addition, even if not specifically mentioned, the two steps may be executed simultaneously or sequentially after the two steps may be changed depending on the characteristics of the steps described in FIG. 9. A DCT domain data of the derived second dynamic range image and DCT domain data of the first dynamic range image are used to derive a prediction coefficient related to the first dynamic range image (S950). Alternative Paper (Article 126 of the Rules) The prediction coefficient related to the first dynamic range image may be calculated by using the correlation of the DCT domain data for the first dynamic range image with respect to the DCT domain data for the second dynamic range image.

Here, the AC coefficients of the DCT domain data for the first dynamic range image and the AC coefficients of the DCT domain data for the second dynamic range image may include a linear polynomial, an exponential function, a logarithmic function, a trigonometric function, and the like. It can have a correlation expressed as a function.

In addition, the DC coefficient of the DCT domain data for the first dynamic range image and the DC coefficient of the DCT domain data for the second dynamic range image have a correlation represented by a prediction curve including a plurality of intervals. Each interval of the prediction curve can be defined by the same or different functions such as polynomials, exponential functions, logarithmic functions, and trigonometric functions.

When the prediction coefficients related to the first dynamic range image are derived, the prediction DCT domain data for the first dynamic range image is derived using the DCT domain data for the second dynamic range image and the prediction coefficients related to the first dynamic range image. Generate one residual coefficient (S960). Here, the residual coefficient may be a DCT coefficient, and the first dynamic range may be defined as a difference value between the DCT domain data for the unknown and the predicted DCT domain data related to the first dynamic range image.

When the residual coefficient is generated, a residual layer codestream including a first dynamic range image-related prediction coefficient and the at least one residual coefficient is generated (S970). Here, the residual layer codestream may include prediction coefficients. Alternative Paper (Article 126 of the Rules) When the residual layer code stream is generated, the base layer code stream and the residual layer code stream are transmitted to the decoder (S980). 10 is an operation flowchart of a decoding method according to an embodiment of the present invention.

The decoding method according to an embodiment of the present invention may be performed by the image decoder illustrated in FIG. 3, but the operation subject is not limited thereto.

The decoder receives a residual layer code stream including the first dynamic range image related prediction coefficients (S1010). The decoder also receives a base layer codestream (S1020) and decodes the received base layer codestream to generate a second dynamic range image (S1030). Here, for convenience of description, steps S1010 and S1020 are described as being sequentially executed, but two steps may be performed simultaneously, step S1020 may be executed first, and step S1010 may be performed later. In addition, even if not specifically mentioned, the two steps may be executed simultaneously or the order of two steps shown sequentially may be changed according to the characteristics of the steps described in FIG. 10.

The decoder derives DCT domain data for the second dynamic range image (S1040), and predicts the first dynamic range image from the prediction coefficients related to the first dynamic range image and the DCT domain data for the second dynamic range image. Derived DCT domain data (S1050).

The decoder finally reconstructs the first dynamic range image by converting the DCT domain data for the first dynamic range image (S1060). At this time, for reconstruction of the first dynamic range image, one dynamic alternative paper is processed through inverse-DCT conversion, inverse-color conversion, and inverse-scale. Convert DCT domain data for an image. 11 is an operational flowchart of a decoding method according to another embodiment of the present invention. The decoding method according to an embodiment of the present invention may be performed by the image decoder illustrated in FIG. 8, but the operation subject is not limited thereto.

The decoder receives a residual layer code stream including the first dynamic range image related prediction coefficients (S1110). The decoder also receives a base layer codestream (S1120) and performs inverse-DCT transformation on the received base layer codestream to derive spatial domain data for the second dynamic range image (S1130). Here, for convenience of description, steps S1110 and S1120 are described as being sequentially executed, but the two steps may be performed simultaneously, or step S1120 may be executed first and step S1110 may be performed later. In addition, even if not specifically mentioned, the two steps may be executed simultaneously or sequentially after the two steps may be changed according to the characteristics of the steps described in FIG. 11.

The decoder then calculates the first dynamic range image-related prediction spatial domain data from the first dynamic range image-related prediction coefficients and the spatial domain data for the second dynamic range image (S1140). The decoder performs inverse -DCT transform on the residual signal included in the residual layer codestream (S1150), and extracts the first dynamic range image from the prediction space data for the first dynamic range image and the inverse -DCT transformed residual signal. Reconfigure (S1160).

Alternative Paper (Article 126 of the Rules) Experiments comparing the performance of the HDR image encoding according to the present invention and the performance of the proposed JPEG XT profile encoding as described above have been performed.

The encoding performance can usually vary depending on the sample image and TM0 adopted. Thus, three sample images of "01", "02", and "03 '' and five TM0 techniques were selected, as shown in Figs. 5 and 6, and a total of 15 cases were tested. And the same conclusion was reached for the performance comparison.

In addition, four image quality evaluation indices were used for objective comparison. In recent years, several researchers have evaluated the performance of JPEG XT profiles. Hanhart et al. Used 13 picture quality indexes to observe the quality of HDR image compression using the JPEG XT profile. This evaluation concluded that 'HDR visible dif ference predictor 2' (腿-VDP-2) is the best quality index for HDR images.

More specifically, Mantel et al. Evaluated the subjective image quality index of Signal_to-noi se rat io (SNR), Mean relative square error (MRSE), and HDR-VDP-2 to determine the subjective quality of HDR images compressed with JPEG XT profiles. And the result of the objective image quality comparison evaluation. This shows that the MRSE quality assessment index provides the most obvious results when using JPEG XT.

Valenzise et al. Compared the performance of HDR-VDP-2 quality index with the performance of Peak SNR (PSNR) and the Structural Similar Index Index (SSIM). This focuses on backward-compatible HDR image coding of HDR images. Through these studies, both PSNR and SSIM quality indexes replace the image reconstruction fidelity for HDR image coding (Article 26). It is concluded that it can be effectively applied to measure.

Cho i et al. Evaluated the performance of the JPEG XT profile by comparing the correlation between the coding performance and the various TM0 profiles using the PSNR quality evaluation index.

In the present invention, PSNR, SSIM, HDR-VDP-2 and MRSE-base SNR were selected for performance comparison, and the PSNR and MRSE-based SNR used in the present invention can be identified through the following notation and definition. The program provided by the authors of each quality index was used for the evaluation when using SS IM and HDR-VDP-2.

Notat ions

Original image, here, M and N are the vertical and horizontal image size

x _r m, n), x (mn)

Red, green, and blue components of the pixel at position (ηι, η) of image X

X = {[("？, n), x _s (m, n), x _b (m, n)]}

Define a coded version of the original image x (Def ini t ion)

^• PSNR

Alternative Site (Article 26)

One

and 舰 (,) =

MRSE-based SNR

where MRdt (A, Λ) =-·> —-ᅳ "― ~ ~.,

Although the above-described embodiments of the present invention have been described mainly for a JPEG system, an image encoding / decoding system to which the present invention is applicable is not limited to JPEG. That is, the present invention may be applicable to any system or apparatus as long as the MPEG system capable of encoding and decoding a video or the encoding or decoding system or apparatus for an image including a still image or a video. Operation of the encoding method and the decoding method according to an embodiment of the present invention can be implemented as a computer-readable program or code on a computer-readable recording medium. Computer-readable recording media include all kinds of recording devices that store data that can be read by a computer system. Also computer readable

Alternative Site (Article 26) Recordable media may be distributed over network coupled computer systems so that computer readable programs or code are stored and executed in a distributed fashion.

In addition, the computer-readable recording medium may include a hardware device specifically configured to store and execute program instructions, such as a ROM, a RAM, a flash memory, or the like. Program instructions can include high-level language code that can be executed by a computer using an interpreter, as well as machine code such as that produced by a compiler.

While some aspects of the invention have been described in the context of a device, it may also represent a description according to the method in which the block or the device is characterized by a method step or a feature of the method step. Similarly, aspects described in the context of the method may also be characterized by the feature of the block or item being interacted with. Some or all of the method steps may be performed by (or using) a hardware device such as, for example, a microprocessor, a programmable computer, or an electronic circuit. In some embodiments, one or more of the most significant method steps may be performed by such an apparatus.

In embodiments, a programmable logic device (eg, a field programmable gate array) may be used to perform some or all of the functionality of the methods described herein. In embodiments, the field programmable gate array may operate in conjunction with a microprocessor to perform one of the methods described herein. In general, the methods are preferably performed by any hardware device.

As described above with reference to a preferred embodiment of the present invention, the substitute paper of the technical field (Article 126) Those skilled in the art will appreciate that various modifications and changes can be made in the present invention without departing from the spirit and scope of the invention as set forth in the claims below.

Alternative Paper (Article 126 of the Rules)

Claims

[Range of request]

[Claim 1]

Converting the first dynamic range image into a second dynamic range image and encoding the second dynamic range image to generate a base layer codestream;

Deriving DCKDiscrete Cosine Transform) domain data for the second dynamic range image;

Deriving DCT domain data for the first dynamic range image; And deriving prediction coefficients related to the first dynamic range image by using the correlation between the DCT domain data for the second dynamic range image and the DCT domain data for the first dynamic range image.

[Claim 2]

The method according to claim 1,

And the first dynamic range image is an HDR high dynamic range (LDR) image and the second dynamic range image is a high dynamic range (LDR) image.

[Claim 3]

The method according to claim 1,

Generating at least one residual coefficient using the DCT domain data for the second dynamic range image and the prediction DCT domain data for the first dynamic range image derived from the prediction coefficients related to the first dynamic range image; And

Generating a residual layer codestream comprising a first dynamic range image-related prediction coefficient and the at least one residual coefficient (Rule 126). method.

[Claim 4]

The method according to claim 1,

Generating the base layer code stream,

Color converting the second dynamic range image;

DCT converting the color converted image;

Quantizing the DCT transformed image; And

Entropy encoding the quantized image.

[Claim 5]

The method according to claim 4,

And an image quality coefficient used in quantizing the DCT transformed image is the same as the image quality coefficient used for quantization of residual DCT domain data.

[Claim 6]

The method according to claim ⁴ ,

Deriving DCT domain data for the second dynamic range image comprises performing inverse quantization on the quantized image.

[Claim 7]

Alternative paper according to claim 1 (Rule 126) An AC coefficient of DCT domain data for the first dynamic range image and an AC coefficient of DCT domain data for the second dynamic range image, having a correlation represented by a polynomial, an exponential function, a logarithmic function, or a trigonometric function Way.

[Claim 8]

The method according to claim 1,

The DC coefficients of the DCT domain data for the first dynamic range image and the DC coefficients of the DCT domain data for the low 12 dynamic range image have a correlation represented by a prediction curve including a plurality of intervals. An interval is defined by the same or different polynomials, exponential functions, logarithmic functions, or trigonometric functions.

[Claim 9]

Receiving a residual layer codestream comprising low U dynamic range image related prediction coefficients;

Receiving a base layer codestream, decoding the received base layer codestream to generate a second dynamic range image;

Deriving DCS (Discrete Cosine Transform) domain data for the second dynamic range image;

Calculating DCT domain data for the first dynamic range image from the prediction coefficients related to the first dynamic range image and the DCT domain data for the second dynamic range image; and

Alternative paper from the DCT domain data for the first dynamic range image (Rule 126) And reconstructing the image.

[Claim 10]

The method according to claim 9,

And the first dynamic range image is an HDR High Dynami c Range image and the second dynamic range image is an LDRC High Dynami c Range image.

[Claim 111]

The method according to claim 9,

Computing DCT domain data for the first dynamic range image comprises: deriving a prediction coefficient and a residual DCT domain data related to the first dynamic range image from the residual layer codestream;

By using the correlation between the DCT domain data for the second dynamic range image and the DCT domain data for the first dynamic range image, the prediction coefficients related to the first dynamic range image and the DCT domain data for the second dynamic range image Calculating predicted DCT domain data for the first dynamic range image from; And

Calculating reconstructed DCT domain data for a first dynamic range image from the predicted DCT domain data for the first dynamic range image and the residual DCT domain data.

[Claim 12]

The method according to claim 11,

AC coefficients of DCT domain data for the first dynamic range image and AC coefficients of DCT domain data for the low 12 dynamic range image are polynomial, exponential function, logarithmic paper (rule 126). An image decoding method having a correlation expressed as a function or a trigonometric function.

[Claim 13]

The method according to claim 11,

The DC coefficient of the DCT domain data for the first dynamic range image and the DC coefficient of the DCT domain data for the second dynamic range image have a correlation represented by a prediction curve including a plurality of intervals, and the angle of the prediction curve The interval is defined by the same or different polynomials, exponential functions, logarithm functions, or trigonometric functions.

[Claim 14]

A base layer processor that converts the first dynamic range image into a second dynamic range image and encodes the second dynamic range image to generate a base layer codestream;

An inverse quantizer for performing inverse quantization on the second dynamic range image quantized by the base layer processor to derive DCS domain data; And

Derive DCT domain data for the first dynamic range image, derive a first dynamic range image related prediction coefficient from DCT domain data for the low 12 dynamic range image and DCT domain data for the first dynamic range image, And an enhancement layer processor for generating a residual layer codestream from the DCT domain data for the first dynamic range image and the prediction coefficients associated with the first dynamic range image.

【Claim 15】 Alternative Sites (Article 126 of the Rules) The method according to claim 14,

The enhancement layer processor,

And a predictor for calculating a prediction coefficient related to the first dynamic range image using the correlation of the DCT domain data for the second dynamic range image and the DCT domain data for the first dynamic range image.

[Claim 16]

The method according to claim 14,

The enhancement layer processor,

Generate at least one residual coefficient using the DCT domain data for the second dynamic range image and the prediction DCT domain data for the first dynamic range image derived from the prediction coefficients related to the first dynamic range image, An image encoder for generating a residual layer codestream comprising range image related prediction coefficients and at least one residual coefficient.

[Claim 17]

The method according to claim 16,

And an image quality factor used for quantization of the layer 2 dynamic range image performed by the base layer processor is the same as a quality factor used for quantization of residual DCT domain data performed by the enhancement layer processor.

[Claim 18]

The method according to claim 14,

The base layer processor is a substitute sheet (rule 126). A color converter for color converting the second dynamic range image;

A DCT converter for DCT converting color converted images;

A quantizer for quantizing the DCT transformed image; And

And an entropy encoder for entropy encoding the quantized image.

[Claim 19]

Receiving a residual layer codestream comprising a first dynamic range image related prediction coefficient;

Receiving a base layer codestream and performing inverse DCS transform on the received base layer codestream to derive spatial domain data for a second dynamic range image; And

Calculating spatial domain data for a first dynamic range image from the prediction coefficients associated with the first dynamic range image and the spatial domain data for the second dynamic range image.

[Claim 20]

The method according to claim 19,

The first dynamic range image is a high dynamic range (HDR) image, and the second dynamic range image is a high dynamic range (LDR) image.

[Claim 21]

The method according to claim 19,

Computing the spatial domain data for the first dynamic range image, substitute paper (Article 126) Performing inverse-DCT transform on the residual signal included in the residual layer codestream;

From the correlation of the DCT domain data for the second dynamic range image and the DCT domain data for the first dynamic range image, from the spatial domain data for the first dynamic range image and the prediction coefficients for the second dynamic range image Calculating predictive spatial domain data for the first dynamic range image; And

Reconstructing a first dynamic range image from the predictive spatial domain data and the inverse -DCT transformed residual signal for the first dynamic range image.

[Claim 22]

The method according to claim 19,

AC coefficients of DCT domain data for the first dynamic range image and AC coefficients of DCT domain data for the second dynamic range image have correlations expressed as polynomials, exponential functions, logarithmic functions, or trigonometric functions. Way.

[Claim 23]

The method according to claim 19,

The DC coefficients of the DCT domain data for the first dynamic range image and the DC coefficients of the DCT domain data for the second dynamic range image have a correlation represented by a prediction curve including a plurality of intervals, the angles of the prediction curves An interval is defined by the same or different polynomials, exponential functions, logarithmic functions, or trigonometric functions. Alternative Paper (Article 126 of the Rules)