SAO Bitstream Coding for Parallel HEVC Processing
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Solution Overview
Problem
The existing moving picture coding and decoding methods using Sample Adaptive Offset (SAO) in the HEVC standard face inefficiencies in processing due to insufficient parallel processing capability and redundant bit length, leading to increased coding and decoding burdens.
Innovation Solution
A moving picture coding method that employs context adaptive binary arithmetic coding for first SAO information with variable probability values and continuous bypass arithmetic coding for second and third SAO information using fixed probability values, allowing for increased parallel processing efficiency by placing coded second and third information after the first information in the bit stream.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If context adaptive binary arithmetic coding with variable probability values is used for all SAO information, then coding efficiency is improved, but processing speed and parallel processing capability deteriorate
Solution Approach 1:
The patent divides SAO information into two categories: first SAO information (sao_type_idx, sao_offset) coded with context adaptive binary arithmetic coding for high compression efficiency, and second SAO information (sao_band_position, sao_offset_sign) coded with bypass arithmetic coding for fast parallel processing. This segmentation allows different coding methods to be applied to different data types based on their characteristics and importance.
Solution Approach 2:
The patent applies different coding strategies to different parts of the SAO information structure. Critical information that requires high precision (sao_type_idx, sao_offset) uses context adaptive coding, while less critical information (sao_band_position, sao_offset_sign) uses simpler bypass coding. This local differentiation optimizes the overall system performance.
2Loss of information
If all SAO information is coded sequentially with context adaptive binary arithmetic coding, then data storability is improved, but device complexity and processing burden increase
Solution Approach 1:
The patent segments the coding process into two parallel paths: one for context adaptive binary arithmetic coding of critical SAO parameters, and another for bypass arithmetic coding of less critical parameters. This reduces the overall processing burden by avoiding sequential handling of all SAO information through the complex context adaptive path.
Solution Approach 2:
The patent performs bypass arithmetic coding for second SAO information in parallel while the context adaptive coding is being performed for first SAO information. This preliminary parallel action reduces the total processing time and complexity without compromising data storability.
3Productivity
If multiple types of SAO information are coded with different methods, then processing efficiency is improved, but device complexity increases
Solution Approach 1:
The patent divides SAO information into two distinct groups with different coding methods, which increases processing efficiency. The complexity is managed by clearly defining the segmentation criteria and using a systematic approach to assign different coding strategies to different information types.
Data Source
AI summary
A moving picture coding method includes: performing context adaptive binary arithmetic coding in which a variable probability value is used, on first information among multiple types of sample adaptive offset (SAO) information used for SAO that is a process of assigning an offset value to a pixel value of a pixel included in an image generated by coding the input image; and continuously performing bypass arithmetic coding in which a fixed probability value is used, on second information and third information among the multiple types of the SAO information, wherein the coded second and third information are placed after the coded first information in the bit stream.