Electronics and Telecommunications Research Institute

Kwangwoon University Research Institute for Industry Cooperation

Sejong University

University-industry Cooperation Group of Kyung Hee University

Korea University Research and Business Foundation

Se-Yoon Jeong

Jin-Soo Choi

Joo-Young Lee

Youn-Hee Kim

Jong-Ho Kim

Seoung-Jun Oh

Dong-Gyu Sim

Jin-Woo Hong

Yung-Lyul Lee

Jin-Woong Kim

Jeong-Il Seo

Seung-Kwon Beack

Chang-Beom Ahn

Kyung-Ae Moon

Dae-Young Jang

Jae-Gon Kim

Hae-Chul Choi

In-Seon Jang

Dong-Kyun Kim

Hui-Yong Kim

Dae-Yeon Kim

Seung-Hyun Cho

Dong-Hyun Kim

Woong Lim

Jin-Wuk Seok

Tae-Jin Lee

Hyoung-Jin Kwon

Dae-Yeol Lee

Ji-Hoon Do

Hyunsuk Ko

Je-Won Kang

Min-Hun Lee

Jee-Hoon Kim

Kwang-Hwan Kim

Han-Sol Choi

Na-Young Kim

Kyu-Heon Kim

Yun-gu Lee

Sung-Chang Lim

Gwang-Hoon Park

Doug-Young Suh

Kyeong-Ok Kang

Min-Woo Park

Seung-won Jung

Dong-Wook Kim

Ho-Chong Park

Jung-Hak Nam

Wonha Kim

Qin Huang

C-c Jay Kuo

Shanglin Yang

Jung-Kyung Lee

Seung-Pyo Shin

Su-Yeol Jeon

Yun-Gu Lee

Disclosed herein are a method, an apparatus, and a storage medium for image encoding/decoding using filtering. The image encoding method and the image decoding method perform classification on a classification unit of a target image, and perform filtering that uses a filter on the classification unit. A classification index of the classification unit is determined, and the classification index indicates a filter to be used for filtering on the classification unit, among multiple filters. The classification index is determined by various coding parameters, such as an encoding mode of the target block and an encoding mode of a neighboring block adjacent to the target block.

Method, apparatus and storage medium for image encoding/decoding using filtering

The present invention discloses an encoding apparatus using a Discrete Cosine Transform (DCT) scanning, which includes a mode selection means for selecting an optimal mode for intra prediction; an intra prediction means for performing intra prediction onto video inputted based on the mode selected in the mode selection means; a DCT and quantization means for performing DCT and quantization onto residual coefficients of a block outputted from the intra prediction means; and an entropy encoding means for performing entropy encoding onto DCT coefficients acquired from the DCT and quantization by using a scanning mode decided based on pixel similarity of the residual coefficients.

Apparatus for encoding and decoding image using adaptive DCT coefficient scanning based on pixel similarity and method therefor

An encoding apparatus extracts features of an image by applying multiple padding operations and multiple downscaling operations to an image represented by data and transmits feature information indicating the features to a decoding apparatus. The multiple padding operations and the multiple downscaling operations are applied to the image in an order in which one padding operation is applied and thereafter one downscaling operation corresponding to the padding operation is applied. A decoding method receives feature information from an encoding apparatus, and generates a reconstructed image by applying multiple upscaling operations and multiple trimming operations to an image represented by the feature information. The multiple upscaling operations and the multiple trimming operations are applied to the image in an order in which one upscaling operation is applied and thereafter one trimming operation corresponding to the upscaling operation is applied.

Method, apparatus, and storage medium using padding/trimming in compression neural network

There are provided a method, apparatus, system, and computer-readable recording medium for image compression. An encoding apparatus performs domain transformation and quantization on feature map information and image information. The encoding apparatus rearranges the result of domain transformation and quantization so as to have a form advantageous to the encoding procedure and encodes the result of rearrangement, thereby generating a bitstream. A decoding apparatus receives the bitstream, decodes the received bitstream, and performs inverse transformation, dequantization, and inverse rearrangement using information transmitted through the bitstream. The result of inverse transformation, dequantization, and inverse rearrangement is used for the machine-learning task of a neural network.

Method, apparatus, system and computer-readable recording medium for feature information

Disclosed herein are a method, an apparatus, and a storage medium for image encoding/decoding. An intra-prediction mode for the target block is derived, and intra-prediction for the target block that uses the derived intra-prediction mode is performed. The intra-prediction mode for the target block is derived using an artificial neural network, and an MPM list for the target block is derived using information about the target block, pieces of information about blocks adjacent to the target block, and the artificial neural network. The artificial neural network outputs one or more available intra-prediction modes. Further, the artificial neural network outputs match probabilities for one or more candidate intra-prediction modes, and each of the match probabilities for the candidate intra-prediction modes indicates a probability that the corresponding candidate intra-prediction mode matches the intra-prediction mode for the target block.

Apparatus, method and storage medium for image encoding/decoding to perform intra prediction using artificial neutral network

There are provided an apparatus, method, system, and recording medium for performing selective encoding/decoding on feature information. An encoding apparatus generates residual feature information. The encoding apparatus transmits the residual feature information to a decoding apparatus through a residual feature map bitstream. The residual feature information is the difference between feature information extracted from an original image and feature information extracted from a reconstructed image. Feature information of the reconstructed image is generated using the reconstructed image. Reconstructed feature information is generated using the feature information of the reconstructed image and reconstructed residual feature information.

Disclosed herein are a method, an apparatus and a storage medium for image encoding/decoding using a binary mask. An encoding method includes generating a latent vector using an input image, generating a selected latent vector component set using a binary mask, and generating a main bitstream by performing entropy encoding on the selected latent vector component set. A decoding method includes generating a selected latent vector component set including one or more selected latent vector components by performing entropy decoding on a main bitstream and generating the latent vector in which the one or more selected latent vector components are relocated by relocating the selected latent vector component set in the latent vector.

Method, apparatus and storage medium for image encoding/decoding using binary mask

Disclosed herein are a method and apparatus for compressing learning parameters for training of a deep-learning model and transmitting the compressed parameters in a distributed processing environment. Multiple electronic devices in the distributed processing system perform training of a neural network. By performing training, parameters are updated. The electronic device may share the updated parameter thereof with additional electronic devices. In order to efficiently share the parameter, the residual of the parameter is provided to the additional electronic devices. When the residual of the parameter is provided, the additional electronic devices update the parameter using the residual of the parameter.

Method and device for providing compression and transmission of training parameters in distributed processing environment

Disclosed herein are a method, apparatus, system, and computer-readable recording medium for image compression. An encoding apparatus performs preprocessing of feature map information, frame packing, frame classification, and encoding. A decoding apparatus performs decoding, frame depacking, and postprocessing in order to reconstruct feature map information. By encoding the feature map information, inter-prediction and intra-block prediction for a frame are performed. The encoding apparatus provides the decoding apparatus with a feature map information bitstream for reconstructing the feature map information along with an image information bitstream.

Method, apparatus, system and computer-readable recording medium for feature map information

A method and apparatus for image compression using a latent variable are provided. The multiple components of the latent variable may be sorted in order of importance. Through sorting, when the feature information of only some of the multiple components is used, the quality of a reconstructed image may be improved. In order to generate a latent variable, the components of which are sorted in order of importance, learning may be performed in various manners. Also, less important information may be eliminated from the latent variable, and processing, such as quantization, may be applied to the latent variable. Through elimination and processing, the amount of data for the latent variable may be reduced.

Method and device for compressing image and neural network using hidden variable

Disclosed herein is a context-adaptive entropy model for end-to-end optimized image compression. The entropy model exploits two types of contexts. The two types of contexts are a bit-consuming context and a bit-free context, respectively, and these contexts are classified depending on the corresponding context requires the allocation of additional bits. Based on these contexts, the entropy model may more accurately estimate the distribution of each latent representation using a more generalized form of entropy models, thus improving compression performance.

Method and apparatus for image processing using context-adaptive entropy model

Disclosed herein are a method and apparatus for measuring video quality based on a perceptually sensitive region. The quality of video may be measured based on a perceptually sensitive region and a change in the perceptually sensitive region. The perceptually sensitive region includes a spatial perceptually sensitive region, a temporal perceptually sensitive region, and a spatio-temporal perceptually sensitive region. Perceptual weights are applied to a detected perceptually sensitive region and a change in the detected perceptually sensitive region. Distortion is calculated based on the perceptually sensitive region and the change in the perceptually sensitive region, and a result of quality measurement for a video is generated based on the calculated distortion.

Method and apparatus for measuring video quality based on detection of change in perceptually sensitive region

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Daejeon, South Korea

Se-Yoon Jeong