Context-Aware Video Stream Generator for Decoder Validation
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Solution Overview
Problem
Current methods for generating test video streams for video decoders are time-consuming and fail to provide complete test coverage, leading to delays in the development and validation of video decoders for new encoding standards.
Innovation Solution
A test stream generator that uses a context-aware pseudo-random generator to produce pseudo-random syntax elements based on decoding context data, combined with a binarizer and decoding process module to generate a compressed video stream, ensuring thorough test coverage and efficient validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional test stream generation methods are used, then test coverage can be achieved, but the process is time-consuming and leads to high time-to-market
Solution Approach 1:
The patent creates a virtual model of the decoding process by implementing a decoding process module within the test stream generator. This module simulates the actual decoder's behavior, generating context data that mirrors what the real decoder would produce. By copying the decoding logic, the system can generate syntactically correct test streams with proper context without requiring actual decoding operations, thus reducing time-to-market while maintaining comprehensive test coverage.
Solution Approach 2:
The system performs preliminary generation of context data and probability distribution laws before actual test stream generation. The decoding process module pre-computes context data based on syntax element values, and the coverage analysis module pre-identifies uncovered decoder items. This preliminary action allows the test stream generator to focus subsequent generation efforts on specifically targeting uncovered areas, reducing overall generation time while ensuring complete coverage.
2Reliability
If comprehensive test coverage is pursued, then all decoder operating modes can be validated, but the generation process becomes slower
Solution Approach 1:
The patent implements a feedback mechanism where the coverage analysis module continuously monitors which decoder items have been tested and which remain uncovered. This coverage context data is fed back to the test stream generator, which adjusts its syntax element generation strategy to target uncovered items. The decoding process module also provides feedback by generating context data that reflects the current state of decoding, enabling dynamic adaptation of the test generation process to achieve complete coverage efficiently.
Solution Approach 2:
The system dynamically adapts the probability distribution laws for syntax element generation based on coverage status. Instead of using static or uniform probability distributions, the system modifies the probability laws to favor syntax element values that are more likely to trigger uncovered decoder items. This dynamic adjustment allows the test stream generation to be both comprehensive and efficient, as the generation process automatically prioritizes untested areas while maintaining syntactic validity.
3Productivity
If pseudo-random syntax elements are generated without context awareness, then generation speed increases, but test coverage quality decreases
Solution Approach 1:
The patent copies the context generation logic from the actual decoder into the test stream generator through the decoding process module. This module implements the same context update rules and probability calculations as the real decoder, ensuring that generated syntax elements are contextually accurate. By copying rather than simplifying the context awareness mechanism, the system maintains high test coverage quality while achieving fast generation through virtualization.
Solution Approach 2:
The system changes the parameters of the pseudo-random generation by using dynamic probability distribution laws instead of fixed distributions. The probability parameters are adjusted based on context data generated by the decoding process module and coverage information from the coverage analysis module. This allows the generation to remain fast and pseudo-random while incorporating context awareness, as the parameter changes guide the random selection toward syntactically valid and coverage-effective values.
Data Source
AI summary
The invention concerns a test stream generator for generating a compressed video stream for testing a video decoder, the generator including: a context-aware pseudo-random generator (202) adapted to generate pseudo-random values of syntax elements based on decoding context data; a decoding process module (206, 206′) adapted to process the syntax element values and to generate the decoding context data and binarization context data; and a binarizer module (204) adapted to encode the syntax element values based on the binarization context data in order to generate the compressed video stream.