Encoding method, decoding method, encoding apparatus, decoding apparatus, and encoding and decoding apparatus

A decoding method, comprising:

• extracting one or more features from one or more reconstructed pixel groups;
• parsing, from a header of a bitstream, a first control parameter for controlling which one of a first machine learning model and a second machine learning model is to be used, the second machine learning model being different from the first machine learning model;
• categorizing the one or more reconstructed pixel groups, wherein (i) when the parsed first control parameter has a predefined first value for using the first machine learning model, the one of more reconstructed pixel groups are categorized using the extracted one or more features and first machine learning model, and (ii) when the written first control parameter does not have the predefined first value, the one of more reconstructed pixel groups are categorized using the extracted one or more features and the second machine learning model;
• performing a decoding process on a current block of image samples included in a current picture using a parameter set corresponding to a result of the categorizing, the parameter set being one of a plurality of predefined parameter sets;
• parsing, from the header of the bitstream, a second control parameter for controlling whether to modify the first machine learning model;
• when the parsed second control parameter does not have a predefined second value for modifying the first machine learning model, performing the categorizing of the one or more reconstructed pixel groups using the extracted one or more features and one of the first machine learning model and the second machine learning model;
• when the parsed second control parameter has the predefined second value:
• parsing, from the header of the bitstream, a third control parameter for modifying the first machine learning model;
• and
• modifying the first machine learning model using the third control parameter;
• and
• when the parsed first control parameter has the predefined first value, performing the categorizing of the one or more reconstructed pixel groups using the extracted one or more features and the modified first machine learning model.

An encoding apparatus, comprising:

• an extractor which extracts one or more features from one or more reconstructed pixel groups;
• a writer which writes, in a header of a bitstream, a first control parameter for controlling which one of a first machine learning model and a second machine learning model is to be used, the second machine learning model being different from the first machine learning model;
• a categorizer which categorizes the one or more reconstructed pixel groups, wherein (i) when the written first control parameter has a predefined first value for using the first machine learning model, the categorizer categorizes the one of more reconstructed pixel groups using the extracted one or more features and the first machine learning model, and (ii) when the written first control parameter does not have the predefined first value, the categorizer categorizes the one of more reconstructed pixel groups using the extracted one or more features and the second machine learning model;
• an encoder which performs an encoding process on a current block of image samples included in a current picture using a parameter set corresponding to a result of the categorizing, the parameter set being one of a plurality of predefined parameter sets,
• wherein the writer writes, in the header of the bitstream, a second control parameter for controlling whether to modify the first machine learning model,
• wherein when the written second control parameter does not have a predefined second value for controlling whether to modify the first machine learning model, the categorizer categorizes the one or more reconstructed pixel groups using the extracted one or more features and one of the first machine learning model and the second machine learning model,
• wherein when the written second control parameter has the predefined second value:
• the writer writes, in the header of the bitstream, a third control parameter for modifying the first machine learning model;
• and
• the categorizer modifies the first machine learning model using the third control parameter, and
• wherein when the written first control parameter has the predefined first value, the categorizer categorizes the one or more reconstructed pixel groups using the extracted one or more features and the modified first machine learning model.

A decoding apparatus, comprising:

• an extractor which extracts one or more features from one or more reconstructed pixel groups;
• a parser which parses, from a header of a bitstream, a first control parameter for controlling which one of a first machine learning model and a second machine learning model is to be used, the second machine learning model being different from the first machine learning model;
• a categorizer which categorizes the one or more reconstructed pixel groups, wherein (i) when the parsed first control parameter has a predefined first value for using the first machine learning model, the categorizer categorizes the one of more reconstructed pixel groups using the extracted one or more features and the first machine learning model, and (ii) when the parsed first control parameter does not have the predefined first value, the categorizer categorizes the one of more reconstructed pixel groups using the extracted one or more features and the second machine learning model;
• a decoder which performs a decoding process on a current block of image samples included in a current picture using a parameter set corresponding to a result of the categorizing, the parameter set being one of a plurality of predefined parameter sets,
• wherein the parser parses, from the header of the bitstream, a second control parameter for controlling whether to modify the first machine learning model,
• wherein when the parsed second control parameter does not have a predefined second value for modifying the first machine learning model, the categorizer categorizes the one or more reconstructed pixel groups using the extracted one or more features and one of the first machine learning model and the second machine learning model,
• wherein when the parsed second control parameter has the predefined second value:
• the parser parses, from the header of the bitstream, a third control parameter for modifying the first machine learning model;
• and
• the categorizer modifies the first machine learning model using the third control parameter, and
• wherein when the parsed first control parameter has the predefined first value, the categorizer categorizes the one or more reconstructed pixel groups using the extracted one or more features and the modified first machine learning model.

An encoding and decoding apparatus, comprising:

• an encoding apparatus including:
• an extractor which extracts one or more features from one or more reconstructed pixel groups;
• a writer which writes, in a header of a bitstream, a first control parameter for controlling which one of a first machine learning model and a second machine learning model is to be used, the second machine learning model being different from the first machine learning model;
• a first categorizer which categorizes the one or more reconstructed pixel groups, wherein (i) when the written first control parameter has a predefined first value for using the first machine learning model, the first categorizer categorizes the one of more reconstructed pixel groups using the extracted one or more features and the first machine learning model, and (ii) when the written first control parameter does not have the predefined first value, the first categorizer categorizes the one of more reconstructed pixel groups using the extracted one or more features and the second machine learning model;
• and
• an encoder which performs an encoding process on a current block of image samples included in a current picture using a parameter set corresponding to a result of the categorizing performing by the first categorizer, the parameter set being one of a plurality of predefined parameter sets,
• wherein the writer writes, in the header of the bitstream, a second control parameter for controlling whether to modify the first machine learning model,
• wherein when the written second control parameter does not have a predefined second value for controlling whether to modify the first machine learning model, the first categorizer categorizes the one or more reconstructed pixel groups using the extracted one or more features and one of the first machine learning model and the second machine learning model,
• wherein when the written second control parameter has the predefined second value;
• the writer writes, in the header of the bitstream, a third control parameter for modifying the first machine learning model;
• and
• the first categorizer modifies the first machine learning model using the third control parameter,
• wherein when the written first control parameter has the predefined first value, the first categorizer categorizes the one or more reconstructed pixel groups using the extracted one or more features and the modified first machine learning model,
• wherein the encoding and decoding apparatus further comprises a decoding apparatus, including:
• a parser which parses, from the header of the bitstream, the first control parameter for controlling which one of the first machine learning model and the second machine learning model is to be used, the second machine learning model being different from the first machine learning model;
• a second categorizer which categorizes the one or more reconstructed pixel groups, wherein (i) when the parsed first control parameter has the predefined first value for using the first machine learning model, the second categorizer categorizes the one of more reconstructed pixel groups using the extracted one or more features and the first machine learning model, and (ii) when the written first control parameter does not have the predefined first value, the second categorizer categorizes the one of more reconstructed pixel groups using the extracted one or more features and the second machine learning model;
• and
• a decoder which performs a decoding process on a current block of image samples included in a current picture using a parameter set corresponding to a result of the categorizing performed by the second categorizer, the parameter set being one of a plurality of predefined parameter sets,
• wherein the parser parses, from the header of the bitstream, the second control parameter for controlling whether to modify the first machine learning model,
• wherein when the parsed second control parameter does not have the predefined second value for modifying the first machine learning model, the second categorizer categorizes the one or more reconstructed pixel groups using the extracted one or more features and one of the first machine learning model and the second machine learning model,
• wherein when the parsed second control parameter has the predefined second value:
• the parser parses, from the header of the bitstream, the third control parameter for modifying the first machine learning model;
• and
• the second categorizer modifies the first machine learning model using the third control parameter, and
• wherein when the parsed first control parameter has the predefined first value, the second categorizer categorizes the one or more reconstructed pixel groups using the extracted one or more features and the modified first machine learning model.