Abstract: The present invention relates to new compounds derived from marine actinomycetes Streptomyces, and the new compound according to the present invention has an inhibitory effect of NO production on BV-2 microglia stimulated with LPS, and thus can be utilized for the prevention and treatment of neuroinflammatory diseases.

Abstract: An inverse quantization method is implemented by an inverse quantization device, the method configured for acquiring quantized coefficients, estimating a quantization parameter in quantization groups or quantization parameter prediction group units, generating an inverse quantization matrix for adaptive quantization, and generating transform coefficients from the quantized coefficients using the quantization parameter and the inverse quantization matrix.

Abstract: Provided is in-loop filtering technology using a trained deep neural network (DNN) filter model. An image decoding method according to an embodiment includes receiving a bitstream of an encoded image, generating reconstructed data by reconstructing the encoded image, obtaining information about a content type of the encoded image from the bitstream, determining a deep neural network (DNN) filter model trained to perform in-loop filtering by using at least one computer, based on the information about the content type, and performing the in-loop filtering by applying the reconstructed data to the determined DNN filter model.

Abstract: Provided is an image encoding or decoding method and device capable of increasing image compression efficiency by determining a differential quantization parameter of a current block based on statistical information of original samples of the current block or samples reconstructed previously to the current block. The image decoding method includes receiving a bitstream, obtaining a predicted quantization parameter value and a quantized transformation coefficient of a current block from the bitstream, obtaining a differential quantization parameter of the current block based on statistical information of samples reconstructed previously to the current block, obtaining a quantization parameter of the current block based on the differential quantization parameter of the current block and the predicted quantization parameter value, and inversely quantizing the quantized transformation coefficient of the current block based on the quantization parameter of the current block.

Abstract: Provided is a prediction image generating technology using a deep neural network (DNN). Provided is an image decoding method including: receiving a bitstream of an encoded image; determining at least one block split from the encoded image; determining neighboring blocks for predicting a current block among the at least one block; generating prediction data of the current block by applying the neighboring blocks to a DNN learning model configured to predict a block of an image by using at least one computer; extracting residual data of the current block from the bitstream; and reconstructing the current block by using the prediction data and the residual data.

Abstract: Provided are an image compressing method including determining a compressed image by performing downsampling using a deep neural network (DNN) on an image; determining a prediction signal by performing prediction based on the compressed image; determining a residual signal based on the compressed image and the prediction signal; and generating a bitstream comprising information about the residual signal, wherein the DNN has a network structure that is predetermined according to training of a downsampling process using information generated in an upsampling process, and an image compressing device for performing the image compressing method. Also, provided are an image reconstructing method of reconstructing a compressed image by using a DNN for upsampling, the compressed image having been compressed by the image compressing method, and an image reconstructing device for performing the image reconstructing method.

Abstract: A method is provided for decoding a sequence of pictures using a gradual refresh technique. In particular, all areas in one picture are gradually encoded or decoded over a plurality of pictures associated with the picture.

Abstract: Disclosed are a novel rhamnolipid compound, an optical isomer thereof, or a pharmaceutically acceptable salt thereof in the present disclosure. The novel rhamnolipid compound, the optical isomer thereof, or the pharmaceutically acceptable salt thereof selectively exhibits cytotoxicity to human cancer cell lines, thereby inhibiting the growth of cancer cells, and thus has particularly excellent preventive or therapeutic effects against the above-mentioned types of cancers.

Abstract: Disclosed are a method for synthesizing a thieno[3,2-b]pyridine-5(4H)-one derivative by using a gold catalyst and a use of the derivative compound, wherein the novel thieno[3,2-b]pyridine-5(4H)-one derivative of the present disclosure, which is a compound synthesized using gold as a catalyst, has fluorescence characteristics with a wide range of emission wavelengths and thus can be helpfully used in various industrial fields, such as physics, chemistry, and biomedicine research.

Abstract: There is provided an image decoding method comprising: deriving a first prediction value of a current block by using at least one sample included in a reference block, obtaining an illumination compensation parameter on the basis of a predetermined reference region, deriving a second prediction value of the current block by applying the illumination compensation parameter to the first prediction value and reconstructing the current block on the basis of the second prediction value.

Abstract: Provided is a method of encoding an image, the method including: determining a subjective quality of the image when the image is compressed; determining at least one degree of compression that changes the subjective quality and is from among degrees of compression indicating how much the image is compressed; and encoding the image by compressing a residual signal of the image, based on compression information according to the determined degree of compression, wherein the subjective quality is determined for each frame by using a Deep Neural Network (DNN). Provided are an image decoding method and an image decoding apparatus for performing the image decoding method for decoding an image by using information encoded according to an image encoding method.

Abstract: An encoding/decoding device according to the present invention derives the residual coefficient of a residual block, calculates a quantization parameter with respect to the residual block, performs inverse quantization for the residual coefficient by means of the quantization parameter, and can restore a residual sample of the residual block by performing inverse transformation for the inverse quantized residual coefficient.

Abstract: Provided is an image encoding or decoding method and device capable of increasing image compression efficiency by determining a differential quantization parameter of a current block based on statistical information of original samples of the current block or samples reconstructed previously to the current block. The image decoding method includes receiving a bitstream, obtaining a predicted quantization parameter value and a quantized transformation coefficient of a current block from the bitstream, obtaining a differential quantization parameter of the current block based on statistical information of samples reconstructed previously to the current block, obtaining a quantization parameter of the current block based on the differential quantization parameter of the current block and the predicted quantization parameter value, and inversely quantizing the quantized transformation coefficient of the current block based on the quantization parameter of the current block.

Abstract: The present invention relates to a novel thieno[3,2-b]pyridine-5(4H)-one derivative compound, a preparation method thereof, and a use thereof. The novel thieno[3,2-b]pyridine-5(4H)-one derivative compound according to the present invention is synthesized via an aza-[3+3] cyclization addition reaction using BOP, wherein the compound is obtained by synthesizing a 4-arylthiophen-3-amine in which aryl groups such as 4-piperidyl-C6H4, 4-pyrrolidyl-C6H4, 4-MeOC6H4, 3,4,5-(MeO)3C6H4, C6H5, 4-C6H5C6H4, and 4-F3COC6H4 are introduced via a synthesis process by a bromination reaction, a hydrolysis reaction, a decarboxylation reaction, and a Suzuki coupling reaction of methyl 3-aminothiophene carboxylate, and by synthesizing 4-arylthiophen-3-amine and mono-methyl fumarate via a formal aza-[3+3] cyclization addition reaction using BOP and conjugation.

Abstract: Provided is a prediction image generating technology using a deep neural network (DNN). Provided is an image decoding method including: receiving a bitstream of an encoded image; determining at least one block split from the encoded image; determining neighboring blocks for predicting a current block among the at least one block; generating prediction data of the current block by applying the neighboring blocks to a DNN learning model configured to predict a block of an image by using at least one computer; extracting residual data of the current block from the bitstream; and reconstructing the current block by using the prediction data and the residual data.

Abstract: Provided are an image compressing method including determining a compressed image by performing downsampling using a deep neural network (DNN) on an image; determining a prediction signal by performing prediction based on the compressed image; determining a residual signal based on the compressed image and the prediction signal; and generating a bitstream comprising information about the residual signal, wherein the DNN has a network structure that is predetermined according to training of a downsampling process using information generated in an upsampling process, and an image compressing device for performing the image compressing method. Also, provided are an image reconstructing method of reconstructing a compressed image by using a DNN for upsampling, the compressed image having been compressed by the image compressing method, and an image reconstructing device for performing the image reconstructing method.

Abstract: A Schottky barrier diode includes: an n? type layer disposed on a first surface of an n+ type silicon carbide substrate; a p+ type region and a p type region disposed on the n? type layer and separated from each other; an anode disposed on the n? type layer, the p+ type region, and the p type region; and a cathode disposed on a second surface of the n+ type silicon carbide substrate. The p type region is in plural, has a hexagonal shape on the plane, and is arranged in a matrix shape, and the n? type layer disposed between the p+ type region and the p type region has a hexagonal shape on the plane and encloses the p type region.

Abstract: Provided are an image compressing method including determining a compressed image by performing downsampling using a deep neural network (DNN) on an image; determining a prediction signal by performing prediction based on the compressed image; determining a residual signal based on the compressed image and the prediction signal; and generating a bitstream comprising information about the residual signal, wherein the DNN has a network structure that is predetermined according to training of a downsampling process using information generated in an upsampling process, and an image compressing device for performing the image compressing method. Also, provided are an image reconstructing method of reconstructing a compressed image by using a DNN for upsampling, the compressed image having been compressed by the image compressing method, and an image reconstructing device for performing the image reconstructing method.

Abstract: Provided are an image compressing method including determining a compressed image by performing downsampling using a deep neural network (DNN) on an image; determining a prediction signal by performing prediction based on the compressed image; determining a residual signal based on the compressed image and the prediction signal; and generating a bitstream comprising information about the residual signal, wherein the DNN has a network structure that is predetermined according to training of a downsampling process using information generated in an upsampling process, and an image compressing device for performing the image compressing method. Also, provided are an image reconstructing method of reconstructing a compressed image by using a DNN for upsampling, the compressed image having been compressed by the image compressing method, and an image reconstructing device for performing the image reconstructing method.

Abstract: Provided is a method of encoding an image, the method including: determining a subjective quality of the image when the image is compressed; determining at least one degree of compression that changes the subjective quality and is from among degrees of compression indicating how much the image is compressed; and encoding the image by compressing a residual signal of the image, based on compression information according to the determined degree of compression, wherein the subjective quality is determined for each frame by using a Deep Neural Network (DNN). Provided are an image decoding method and an image decoding apparatus for performing the image decoding method for decoding an image by using information encoded according to an image encoding method.