Video Transcoder Perceptual Classification for Bit Allocation
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Solution Overview
Problem
Conventional perceptual quantization approaches in video transcoders face challenges in maintaining consistent quality of experience (QoE) for transcoded video, especially when dealing with video inputs encoded by external encoders with unknown characteristics, leading to inefficient bit allocation and potential waste of bits.
Innovation Solution
The system employs a video transcoding method that uses perceptual processing techniques, including a video decoder, perceptual classification component, rate control, motion estimation, and quantization, to classify macroblocks based on distortion imperceptibility and adjust quantization parameters using information from the input bitstream, ensuring optimal bit allocation and enhanced QoE.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional perceptual quantization is used in video transcoders, then bit allocation can be performed based on spatial and temporal variations, but the quality of experience becomes inconsistent when dealing with video inputs from external encoders with unknown characteristics
Solution Approach 1:
The patent uses feedback from the decoded video signal itself to adjust quantization decisions. By analyzing the actual decoded macroblocks and comparing them with reference frames, the system adapts its distortion imperceptibility classification to the specific characteristics of the input video, rather than relying on assumptions about the external encoder's behavior. This feedback mechanism ensures reliable QoE consistency regardless of the source encoder's characteristics.
Solution Approach 2:
The system dynamically changes quantization parameters based on actual decoded video characteristics rather than fixed assumptions. By computing distortion metrics from the actual decoded signal and adjusting QP values accordingly, the transcoder adapts to different input sources while maintaining consistent quality. This parameter adaptation resolves the contradiction by making the system's behavior dependent on actual observations rather than uncertain external characteristics.
2Productivity
If conventional perceptual quantization classifies macroblocks based on spatial and temporal variations, then bit allocation can be optimized, but bits are wasted when the external encoder already applied similar perceptual processing
Solution Approach 1:
The system extracts feedback information from the input bitstream including QP values and macroblock types to determine whether the external encoder already applied perceptual processing. This feedback allows the transcoder to avoid redundant bit allocation in regions where distortion is already imperceptible, improving bit allocation efficiency while preventing bit waste.
Solution Approach 2:
Instead of applying full perceptual quantization analysis to all macroblocks, the system performs partial analysis only where needed based on feedback from the external encoder's processing. By identifying regions where the external encoder already optimized quality, the transcoder可以避免 redundant processing and bit allocation, achieving efficient bit usage without excessive action.
3Measurement precision
If the transcoder performs full perceptual analysis on all macroblocks, then classification accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the video processing into distinct stages: extraction of feedback information from bitstream, selective application of perceptual analysis only to relevant macroblocks, and separate processing paths for different MB types. This segmentation reduces overall computational complexity by avoiding redundant analysis while maintaining classification accuracy where it matters most.
Solution Approach 2:
The system performs partial perceptual analysis only on macroblocks where it is necessary, rather than analyzing all macroblocks equally. By using feedback from the external encoder to identify regions requiring analysis, the system achieves sufficient classification accuracy with reduced computational complexity, avoiding excessive processing effort.
Data Source
AI summary
Systems and methods of video transcoding that employ perceptual processing techniques for enhancing the perceptual quality of transcoded video information, communications, entertainment, and other video content. Such systems and methods of video transcoding are operative to perform perceptual processing of an input video bitstream using predetermined information carried by the input bitstream. Having performed such perceptual processing of the input bitstream, the perceptual quality of transcoded video delivered to an end user is significantly improved.