Arithmetic Range Encoding for Lower-Bitrate Video Compression
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Solution Overview
Problem
Current video data compression and decompression systems face inefficiencies in entropy encoding, particularly with existing techniques like CABAC, which do not fully optimize bit rate and image quality, especially at higher bit depths and low quantization parameters.
Innovation Solution
The implementation of advanced entropy encoding techniques, such as modified CABAC processes and fixed-bit encoding schemes, to improve compression efficiency by adapting context modeling and bit allocation, allowing for higher bit depth operations without significant loss in resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If CABAC entropy encoding is used, then compression efficiency is improved, but complexity of the encoding process increases
Solution Approach 1:
The patent divides the encoding process into distinct stages: context modeling, binary arithmetic coding, and range normalization. By segmenting the complex CABAC process into manageable components, the patent reduces implementation complexity while preserving compression efficiency. Each stage can be independently optimized and implemented.
Solution Approach 2:
The patent implements dynamic context modeling where context probabilities are updated adaptively based on previously encoded data. This dynamic adjustment allows the encoder to optimize for specific data patterns and characteristics, improving compression efficiency without requiring a completely complex static structure for all possible scenarios.
2Manufacturing precision
If higher bit depth operations are performed, then image quality is improved, but bit rate increases
Solution Approach 1:
The patent employs parameter optimization techniques that adjust encoding parameters dynamically based on the input data characteristics. By changing parameters such as context table sizes, probability update frequencies, and arithmetic coding precision, the patent achieves high image quality at lower bit rates by optimizing the balance between precision and data representation efficiency.
3Quantity of substance
If context adaptive binary arithmetic coding is used, then data size is reduced, but computational complexity increases
Solution Approach 1:
The patent performs preliminary context modeling and probability estimation before the actual arithmetic coding process. By pre-computing context probabilities and preparing encoding tables in advance, the patent reduces the computational burden during the actual encoding phase, thereby reducing real-time computational complexity while maintaining data size reduction benefits.
Solution Approach 2:
The patent implements a hybrid approach where full precision arithmetic coding is applied only when necessary, while using simplified coding methods for data patterns that can be efficiently encoded with lower precision. This partial application of complex methods reduces overall computational complexity while maintaining effective data size reduction for the most beneficial cases.
Data Source
AI summary
A method of data encoding input data values of a data set for encoding includes selecting one of a plurality of complementary sub-ranges of a set of code values according to the value of a current input data value, the set of code values being defined by a range variable, assigning the current input data value to a code value within the selected sub-range, modifying the set of code values in dependence upon the assigned code value and the size of the selected sub-range, detecting whether the range variable defining the set of code values is less than a predetermined minimum size and if so, successively increasing the range variable so as to increase the size of the set of code values until it has at least the predetermined minimum size and outputting an encoded data bit in response to each such size-increasing operation, and after encoding a group of input data values, setting the range variable to a value selected from a predetermined subset of available range variable values, each value in the subset having at least one least significant bit equal to zero.