An encoder includes circuitry and memory connected to the circuitry. In operation, the circuitry: derives a correction parameter using only a neighboring reconstructed image that neighbors a processing unit which has a determined size and is located at an upper left of a current block to be processed in an image, among neighboring reconstructed images that neighbor the current block, and performs correction processing of the current block based on the correction parameter derived, when the current block has a size larger than the determined size.

An encoder includes circuitry and memory connected to the circuitry. In operation, the circuitry: derives a correction parameter using only a neighboring reconstructed image that neighbors a processing unit which has a determined size and is located at an upper left of a current block to be processed in an image, among neighboring reconstructed images that neighbor the current block, and performs correction processing of the current block based on the correction parameter derived, when the current block has a size larger than the determined size.

A video image decoding device receives, as the code string to be decoded, a first code string to be decoded including information based on an encoded residual coefficient and header information or a second code string to be decoded including a residual image obtained in encoding the code string to be decoded and header information. The video image decoding device, when the code string to be decoded that is received by the receiver is the first code string to be decoded, adds the residual decoded image and the predictive image to each other to generate and output a reconstructed image and, when the code string to be decoded received by the receiver is the second code string to be decoded, adds a residual image included in the second code string to be decoded and the predictive image to each other to generate and output a reconstructed image.

A video image decoding device receives, as the code string to be decoded, a first code string to be decoded including information based on an encoded residual coefficient and header information or a second code string to be decoded including a residual image obtained in encoding the code string to be decoded and header information. The video image decoding device, when the code string to be decoded that is received by the receiver is the first code string to be decoded, adds the residual decoded image and the predictive image to each other to generate and output a reconstructed image and, when the code string to be decoded received by the receiver is the second code string to be decoded, adds a residual image included in the second code string to be decoded and the predictive image to each other to generate and output a reconstructed image.

An image decoding apparatus obtain pieces of coded data that is included in a bitstream and generated by coding tiles. Tile boundary independence information is further obtained from the bitstream, with the tile boundary independence information indicating whether each of boundaries between the tiles is one of a first boundary or a second boundary. The pieces of coded data are decoded to generate image data of the tiles. Image data of a first tile is generated by decoding a first code string included in first coded data with reference to decoding information of a decoded tile when the tile boundary independence information indicates the first boundary, and by decoding the first code string without referring to the decoding information of the decoded tile when the tile boundary independence information indicates the second boundary.

An image decoding apparatus obtain pieces of coded data that is included in a bitstream and generated by coding tiles. Tile boundary independence information is further obtained from the bitstream, with the tile boundary independence information indicating whether each of boundaries between the tiles is one of a first boundary or a second boundary. The pieces of coded data are decoded to generate image data of the tiles. Image data of a first tile is generated by decoding a first code string included in first coded data with reference to decoding information of a decoded tile when the tile boundary independence information indicates the first boundary, and by decoding the first code string without referring to the decoding information of the decoded tile when the tile boundary independence information indicates the second boundary.

According to an embodiment of the present invention, there is provided an image decoding method including: obtaining an arithmetic-coded symbol stream from an image stream; identifying a symbol segment from the symbol stream according to symbol segment information signaled from an encoding apparatus; selecting an arithmetic decoding process corresponding to the symbol segment; and collectively processing one or more symbols among multiple symbols included in the symbol segment, in association with the selected arithmetic decoding process.

A decoder includes circuitry configured to receive a bitstream identify, in the bitstream, a current frame, wherein the current frame includes a first region and a third region, detect, in the bitstream, an indication that the first region is encoded according to a lossless encoding protocol, and decode the current frame, wherein decoding the current frame further comprises decoding the first region using a lossless decoding protocol corresponding to the lossless encoding protocol.

Occupancy networks enable efficient and flexible point cloud compression. In addition to the voxel-based representation, occupancy networks are able to handle points, meshes, or projected images of 3D objects, making them very flexible in terms of input signal representation. The probability of occupancy of positions is estimated using occupancy networks instead of sparse convolutional neural networks. A compression implementation using occupancy network enables scalability with infinite reconstruction resolution.

Occupancy networks enable efficient and flexible point cloud compression. In addition to the voxel-based representation, occupancy networks are able to handle points, meshes, or projected images of 3D objects, making them very flexible in terms of input signal representation. The probability of occupancy of positions is estimated using occupancy networks instead of sparse convolutional neural networks. A compression implementation using occupancy network enables scalability with infinite reconstruction resolution.