Adaptive Video Encoding via Gradient-Based Region Segmentation
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Solution Overview
Problem
Current video codecs, such as H.264 and H.265, face limitations in achieving efficient data compression and decompression, particularly in handling high-resolution video data across varying bit rates, which affects video quality and compression ratios.
Innovation Solution
The proposed solution involves a video data compression and decompression system that uses a controller to select operation modes, including block sizes and encoding types, and employs a predictor to generate residual images, followed by discrete cosine transforms, quantization, and entropy encoding, with optional secondary transforms and filtering processes to optimize encoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional video codecs (H.264/H.265) use regular macroblocks for compression, then encoding complexity is reduced, but compression efficiency and video quality deteriorate at high resolutions
Solution Approach 1:
The patent divides the image into multiple regions based on gradient analysis, transforming the uniform macroblock structure into region-specific blocks. This segmentation allows different encoding strategies to be applied to different image areas, improving compression efficiency while maintaining manageable complexity through systematic region classification.
Solution Approach 2:
The patent applies different encoding precision and transform sizes to different regions based on their gradient characteristics. High-gradient regions receive different treatment compared to low-gradient regions, optimizing the balance between compression efficiency and encoding complexity by adapting local encoding quality to local image content.
2Productivity
If video codecs increase compression ratio for high-resolution data, then data transmission efficiency improves, but video quality deteriorates
Solution Approach 1:
The patent preserves video quality by applying different compression strengths to different regions. Important regions with high gradients maintain higher quality through appropriate transform selection, while less important regions accept higher compression. This local quality adaptation enables overall improved compression ratio without uniform quality degradation.
Solution Approach 2:
The patent dynamically changes transform parameters (size and type) based on gradient analysis of each region. By adapting transform parameters to local image characteristics, the system achieves better energy compaction and compression efficiency while maintaining visual quality in critical areas, resolving the trade-off between compression ratio and video quality.
3Device complexity
If video codecs use fixed transform sizes for all blocks, then encoding complexity is reduced, but compression efficiency deteriorates for varying image content
Solution Approach 1:
The patent introduces dynamic transform size selection based on gradient analysis of each region. Instead of fixed transform sizes, the system adapts transform dimensions to match local image characteristics, improving compression efficiency through better energy compaction while maintaining reasonable complexity through systematic adaptation rules.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances video data compression efficiency, allowing for improved video quality and increased compression ratios, particularly in high-resolution scenarios, by effectively managing energy in residual images and adapting transforms and filters based on image properties.
Implementation Method 1
The proposed solution involves a video data compression and decompression system that uses a controller to select operation modes, including block sizes and encoding types, and employs a predictor to generate residual images, followed by discrete cosine transforms, quantization, and entropy encoding
Data Source
AI summary
Apparatus comprises an image data encoder to encode a current image region of an image, the image data encoder being operable in at least two modes of operation; a controller to control a mode of operation of the image data encoder in dependence upon the encoded data for the current image region meeting a predetermined criterion; and prediction circuitry configured to predict, from one or more properties of one or more image regions other than the current image region, whether the encoded data for the current image region will meet the predetermined criterion.