Techniques and apparatus for coarse granularity scalable lifting for point-cloud attribute coding
A method, computer system, and computer-readable medium are provided for point cloud attribute coding by at least one processor. Data associated with a point cloud is received. The received data is transformed through a lifting decomposition based on enabling a scalable coding of attributes associated with the lifting decomposition. The point cloud is reconstructed based on the transformed data.
1. A method of point cloud attribute coding, the method being performed by at least one processor, and the method comprising:
receiving data associated with a point cloud;
transforming the received data through a lifting decomposition based on enabling a scalable coding of attributes associated with the lifting decomposition by iterations, over a set of defined quantization-levels for each of lifting transform coefficients, in which a process is repeated on residual coefficients resulting from repeatedly subtracting reconstructed layers with previous quantization levels; and
reconstructing the point cloud based on the transformed data.
2. The method of claim 1 , wherein smaller lifting coefficients are generated by the lifting decomposition for higher level-of-detail layers associated with the received data based on one or more context models being used for different level-of-detail layers.
3. The method of claim 1 , wherein smaller quantized coefficients are generated by the lifting decomposition for higher quantization parameters based on one or more context models being used for different quantization parameters.
4. The method of claim 1 , wherein one or more context models are used for different layers of coarse granular scalability for the lifting decomposition based on minimizing noise between coefficients of the lifting decomposition.
5. The method of claim 1 , wherein one or more context models are used based on values or a function of values of reconstructed samples from corresponding locations in lower quantization level layers associated with the received data.
6. The method of claim 1 , wherein one or more context models are used based on values or a function of values of reconstructed samples from corresponding locations in lower level-of-detail layers having the same quantization level.
7. The method of claim 1 , wherein the lifting decomposition is applied based on adaptively switching look-up-tables for symbol-index coding based on dictionary-based coding being used.
8. A computer system for point cloud attribute coding, comprising:
at least one memory configured to store computer program code; and
at least one processor configured to access the at least one memory and operate according to the computer program code, the computer program code comprising:
receiving code configured to cause the at least one processor to receive data associated with a point cloud;
transforming code configured to cause the at least one processor to transform the received data through a lifting decomposition based on enabling a scalable coding of attributes associated with the lifting decomposition by iterations, over a set of defined quantization-levels for each of lifting transform coefficients, in which a process is repeated on residual coefficients resulting from repeatedly subtracting reconstructed layers with previous quantization levels; and
reconstructing code configured to cause the at least one processor to reconstruct the point cloud based on the transformed data.
9. The computer system of claim 8 , wherein smaller lifting coefficients are generated by the lifting decomposition for higher level-of-detail layers associated with the received data based on one or more context models being used for different level-of-detail layers.
10. The computer system of claim 8 , wherein smaller quantized coefficients are generated by the lifting decomposition for higher quantization parameters based on one or more context models being used for different quantization parameters.
11. The computer system of claim 8 , wherein one or more context models are used for different layers of coarse granular scalability for the lifting decomposition based on minimizing noise between coefficients of the lifting decomposition.
12. The computer system of claim 8 , wherein one or more context models are used based on values or a function of values of reconstructed samples from corresponding locations in lower quantization level layers associated with the received data.
13. The computer system of claim 8 , wherein one or more context models are used based on values or a function of values of reconstructed samples from corresponding locations in lower level-of-detail layers having the same quantization level.
14. The computer system of claim 8 , wherein the lifting decomposition is applied based on adaptively switching look-up-tables for symbol-index coding based on dictionary-based coding being used.
15. A non-transitory computer-readable storage medium storing instructions that cause at least one processor to:
receive data associated with a point cloud;
transform the received data through a lifting decomposition based on enabling a scalable coding of attributes associated with the lifting decomposition by iterations, over a set of defined quantization-levels for each of lifting transform coefficients, in which a process is repeated on residual coefficients resulting from repeatedly subtracting reconstructed layers with previous quantization levels; and
reconstruct the point cloud based on the transformed data.
16. The computer readable medium of claim 15 , wherein smaller lifting coefficients are generated by the lifting decomposition for higher level-of-detail layers associated with the received data based on one or more context models being used for different level-of-detail layers.
17. The computer readable medium of claim 15 , wherein smaller quantized coefficients are generated by the lifting decomposition for higher quantization parameters based on one or more context models being used for different quantization parameters.
18. The computer readable medium of claim 15 , wherein one or more context models are used for different layers of coarse granular scalability for the lifting decomposition based on minimizing noise between coefficients of the lifting decomposition.
19. The computer readable medium of claim 15 , wherein one or more context models are used based on values or a function of values of reconstructed samples from corresponding locations in lower quantization level layers associated with the received data.
20. The computer readable medium of claim 15 , wherein one or more context models are used based on values or a function of values of reconstructed samples from corresponding locations in lower level-of-detail layers having the same quantization level.