Video Encoding QP Adjustment for Background Frames
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Solution Overview
Problem
Current video encoding technologies, such as HEVC, lack an effective method for setting the quantization parameter (QP) for long-term reference frames, leading to poor encoding quality due to the neglect of video content changes and reliance solely on short-term reference frame QP ranges.
Innovation Solution
A method that calculates a statistical characteristic from previous video frames to determine a QP for encoding a background long-term reference frame, considering the variance of centroid values and average coding distortions, and uses this to estimate the probability of image blocks using the background frame as a reference, thereby adjusting the QP for improved encoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the quantization parameter for background frame is determined only according to short-term reference frame QP range, then the encoding process is simple, but the encoding quality is poor due to neglecting video content changes
Solution Approach 1:
The patent performs preliminary analysis of video content changes and motion characteristics before determining the QP for background frames. By pre-calculating statistical characteristics (variance of centroid values, average coding distortion) from previous K frames, the system prepares encoding parameters in advance, allowing for quality improvement without excessive complexity during actual encoding.
Solution Approach 2:
The patent dynamically adjusts the QP parameter for background frames based on calculated statistical characteristics of video content changes. Instead of using a fixed or simple range-based QP determination, the system modifies the QP value according to measured variance of centroid values and average coding distortion, enabling adaptive quality optimization.
2Measurement precision
If motion search is performed on reconstructed frames to find matching reference blocks, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary classification of image blocks into background and foreground types before motion search. By pre-identifying background blocks with stable characteristics (low variance of centroid values), the system can apply simplified reference frame selection for these blocks, reducing computational complexity while maintaining prediction accuracy for static regions.
Solution Approach 2:
The patent applies different encoding strategies to different regions: background blocks (with low motion variance) use simplified reference frame selection from long-term reference frames, while foreground blocks (with high motion variance) undergo full motion search. This local differentiation optimizes prediction accuracy for each region type while controlling overall computational complexity.
3Manufacturing precision
If the quantization parameter is adjusted to account for video content changes, then encoding quality improves, but bit rate may increase
Solution Approach 1:
The patent adjusts the QP parameter dynamically based on calculated statistical characteristics (variance of centroid values and average coding distortion) to optimize encoding quality. By increasing QP for background blocks with low motion variance and maintaining lower QP for foreground blocks with high motion variance, the system improves overall quality while controlling bit rate through selective parameter adjustment.
Solution Approach 2:
The patent applies different QP values to different block types: background blocks (low motion variance) receive higher QP values for efficient compression, while foreground blocks (high motion variance) maintain lower QP values for better quality. This localized quality control improves overall encoding quality without uniformly increasing bit rate across all blocks.
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AI summary
An image encoding and decoding method, and a video encoding and decoding method and apparatus are disclosed. The image encoding method includes: obtaining a first statistical characteristic of previous K frames of images of a video sequence; determining a first background frame; determining, according to the first statistical characteristic, a QP used for encoding the first background frame; encoding the first background frame according to the QP, to obtain a first background long-term reference frame; and encoding the (K+1)th frame of image of the video sequence to the (K+L)th frame of image of the video sequence according to the first background long-term reference frame. When encoding is performed on the (K+1)th frame of image of the video sequence to the (K+L)th frame of image of the video sequence, the used QP for encoding the background long-term reference frame is determined according to the first statistical characteristic related to video content of the previous K frames of images. This improves overall video encoding quality.