Abstract: Provided are an inter prediction method and a motion compensation method. The inter prediction method includes: performing inter prediction on a current image by using a long-term reference image stored in a decoded picture buffer; determining residual data and a motion vector of the current image generated via the inter prediction; and determining least significant bit (LSB) information as a long-term reference index indicating the long-term reference image by dividing picture order count (POC) information of the long-term reference image into most significant bit (MSB) information and the LSB information.

Abstract: Encoding an image using a non-encoding region of the image, a block-based encoding region of the image, and a pixel-based encoding region of the image.

Abstract: Provided are an inter prediction method and a motion compensation method. The inter prediction method includes: performing inter prediction on a current image by using a long-term reference image stored in a decoded picture buffer; determining residual data and a motion vector of the current image generated via the inter prediction; and determining least significant bit (LSB) information as a long-term reference index indicating the long-term reference image by dividing picture order count (POC) information of the long-term reference image into most significant bit (MSB) information and the LSB information.

Abstract: Provided are a method and apparatus for encoding or decoding a 360-degree image. An image decoding method and apparatus according to an embodiment include: obtaining image data from a bitstream; decoding a first region of a projection image corresponding to a non-clipping region of a 360-degree image from the image data; obtaining information about a clipping region of the 360-degree image from the bitstream; determining a second region of the projection image, based on the information about the clipping region; and converting the projection image including the first region and the second region into the 360-degree image.

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: Accordingly embodiments herein disclose a method for producing 360 degree image content on a rectangular projection. The method includes detecting whether at least one discontinuous boundary is present in the 360-degree image content, wherein the at least one discontinuous boundary is detected using the packing of one or more projection segments. Further, the method includes obtaining one or more reconstructed blocks from the 360-degree image content. The reconstructed blocks are generated from the 360 degree image content. Further, the method includes performing one of enabling at least one in-loop filtering function on the one or more reconstructed blocks of the 360 degree image content when the at least one of image content discontinuous boundary is not present and disabling at least one in-loop filtering function on the one or more reconstructed blocks of the 360 degree image content located around the at least one discontinuous boundary.

Abstract: Provided are a method and apparatus for encoding or decoding a 360-degree image. An image decoding method and apparatus according to an embodiment include: obtaining image data from a bitstream; decoding a first region of a projection image corresponding to a non-clipping region of a 360-degree image from the image data; obtaining information about a clipping region of the 360-degree image from the bitstream; determining a second region of the projection image, based on the information about the clipping region; and converting the projection image including the first region and the second region into the 360-degree image.

Abstract: Provided are an inter prediction method and a motion compensation method. The inter prediction method includes: performing inter prediction on a current image by using a long-term reference image stored in a decoded picture buffer; determining residual data and a motion vector of the current image generated via the inter prediction; and determining least significant bit (LSB) information as a long-term reference index indicating the long-term reference image by dividing picture order count (POC) information of the long-term reference image into most significant bit (MSB) information and the LSB information.

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: Accordingly embodiments herein disclose a method for producing 360 degree image content on a rectangular projection. The method includes detecting whether at least one discontinuous boundary is present in the 360-degree image content, wherein the at least one discontinuous boundary is detected using the packing of one or more projection segments. Further, the method includes obtaining one or more reconstructed blocks from the 360-degree image content. The reconstructed blocks are generated from the 360 degree image content. Further, the method includes performing one of enabling at least one in-loop filtering function on the one or more reconstructed blocks of the 360 degree image content when the at least one of image content discontinuous boundary is not present and disabling at least one in-loop filtering function on the one or more reconstructed blocks of the 360 degree image content located around the at least one discontinuous boundary.

Abstract: Provided is a method, performed by a terminal, of providing content includes determining whether at least one content is included in preference content, based on information about the at least one content provided to the terminal and preference information of a user of the terminal about content; content, which is not included in the preference content, is present in the at least one content, transmitting, to a server, a request to change the first content to second content included in the preference content based on previously stored information about the preference content; and outputting the second content received from the server, in response to the request.

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: 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: Provided are a method and apparatus for encoding or decoding a 360-degree image. According to an image decoding method and apparatus according to an embodiment, an original 360-degree image is reconstructed by acquiring image data from a bitstream, decoding a projection image of a 360-degree image from the image data, converting the projection image into the 360-degree image, acquiring rotation information of the 360-degree image from the bitstream, and rotating the 360-degree image based on the rotation information.

Abstract: Provided are an inter prediction method and a motion compensation method. The inter prediction method includes: performing inter prediction on a current image by using a long-term reference image stored in a decoded picture buffer; determining residual data and a motion vector of the current image generated via the inter prediction; and determining least significant bit (LSB) information as a long-term reference index indicating the long-term reference image by dividing picture order count (POC) information of the long-term reference image into most significant bit (MSB) information and the LSB information.

Abstract: Provided is an image processing method including: generating an omnidirectional spherical image; determining information regarding a director's view that indicates some regions of the omnidirectional spherical image; generating a projection image by projecting the omnidirectional spherical image to a development view of a polyhedron; dividing, from the projection image, a director's view projection image corresponding to the director's view, based on the information regarding the director's view; reshaping the director's view projection image into a director's view rectangular image by moving a location of at least one of pixels of the director's view projection image; and generating a bitstream which the director's view rectangular image is encoded into.

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: Embodiments herein disclose a method for producing 360 degree image content on a rectangular projection in an electronic device. The method includes obtaining a 360 degree image content represented by packing one or more projection segments arranged in a rectangular projection. The method includes detecting whether at least one discontinuous boundary is present in the 360-degree image content. The at least one discontinuous boundary is detected using the packing of one or more projection segments. The method includes applying at least one padding information on the at least one discontinuous boundary. The method includes producing another 360 degree image content on the rectangular projection in the electronic device based on the padding information.