Quantized Coefficient Optimization for Video Encoding
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Solution Overview
Problem
Current video encoding techniques face challenges in real-time rate-distortion optimization due to complex computations required for optimizing coefficients, especially in complex encoding algorithms, leading to inefficiencies in data transmission and quality preservation.
Innovation Solution
The implementation of an apparatus with an encoder and transcoder that optimizes quantized coefficients using a lambda-weighted cost function, enabling real-time processing and conversion between different encoding techniques like CAVLC and CABAC, allowing for efficient rate-distortion optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If complex encoding algorithms are used for rate-distortion optimization, then the quality of video encoding is improved, but the computational complexity increases making real-time processing difficult
Solution Approach 1:
The patent pre-calculates and stores rate cost values for different coefficient quantization scenarios before actual encoding. By preparing these cost tables in advance, the encoder avoids performing complex rate calculations during real-time encoding, thus reducing computational complexity while maintaining encoding quality optimization
Solution Approach 2:
The patent pre-computes distortion costs and stores them in lookup tables before encoding begins. This beforehand preparation cushions against the computational burden during real-time operation, allowing the encoder to quickly retrieve pre-calculated distortion values without performing complex calculations during actual encoding processes
2Productivity
If full rate-distortion optimization with encoding cost calculation is performed, then the encoding efficiency is improved, but the processing time increases preventing real-time operation
Solution Approach 1:
The patent creates simplified copies of the actual encoding process by pre-calculating rate costs and distortion costs separately, then combining them during optimization. This copying approach allows the system to perform rate-distortion optimization using pre-computed data rather than performing full encoding simulations, significantly reducing processing time while maintaining encoding efficiency
Solution Approach 2:
The patent segments the rate-distortion optimization process into separate components: rate cost calculation, distortion cost calculation, and final optimization. By dividing the complex optimization task into independent segments that can be pre-computed and stored, the system achieves real-time processing capability while maintaining high encoding efficiency
Data Source
AI summary
Apparatuses and methods for optimizing rate-distortion costs in a signal are disclosed. An apparatus may comprise a quantization block that may be configured to generate a plurality of candidates for each of a plurality of coefficients. The quantization block may further generate a respective plurality of arcs based, at least in part, on the plurality of candidates. The quantization block may be configured to determine which of the plurality of arcs has a lowest cost using a trellis optimization technique. Fractional bit estimations may be used to calculate rate, and inverse lambda may be used to calculate candidate coefficients.