Video Coding Pre-Processing for Noise-Aware Compression

The patent applies pre-processing techniques before encoding to remove noise and reduce video resolution. This preliminary action prepares the video data in advance, reducing the burden on the encoding process and preventing quality degradation while maintaining coding efficiency. The pre-processing step handles noise removal and resolution adjustment before the main compression workflow.

The patent converts the harmful effect of noise in video data into a benefit by using noise modeling and removal techniques. Instead of letting noise degrade quality, the system models the noise characteristics and removes it, transforming a harmful factor into an opportunity for quality enhancement while improving compression efficiency.

The patent uses parameter-based noise modeling where noise characteristics are represented through specific parameters that can be estimated and manipulated. By changing and optimizing these parameters during pre-processing, the system achieves quality enhancement without requiring overly complex processing, as the noise removal is guided by parameter optimization rather than exhaustive computation.

The patent incorporates feedback mechanisms where the processing results are evaluated and used to adjust subsequent processing steps. This feedback loop allows the system to achieve quality enhancement efficiently by learning from intermediate results and optimizing the pre-processing parameters, reducing the need for excessive processing complexity.

The patent performs preliminary resolution adjustment and noise removal before encoding, which allows the system to work with reduced data amounts while preserving perceptual quality. By preparing the video data in advance with appropriate resolution scaling and noise reduction, the system reduces the quantity of data that needs to be processed and transmitted, while maintaining the essential perceptual quality characteristics.

The patent optimizes video parameters such as resolution and frame rate based on perceptual quality requirements rather than simply maintaining original high specifications. By changing these parameters intelligently based on content analysis and quality requirements, the system reduces data amount while preserving the perceptual quality that matters most to viewers.