Scene Change Detection in Video Encoding via Macroblock Distribution
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Solution Overview
Problem
Conventional scene change detection in video encoding requires decoding compressed video, leading to computational inefficiencies and fails to adjust bit rates for improved image quality, often misidentifying gradual changes as scene changes and not effectively managing bit rates.
Innovation Solution
A scene change identification process that operates during video encoding, determining scene changes based on the distribution of macroblock types and allocating more bits to frames without changing frame types, using a threshold-based method to identify scene changes and generate side information for improved bit rate control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional scene change detection decodes compressed video to analyze motion vectors and color data, then scene change identification accuracy is improved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent applies preliminary action by performing scene change detection during the encoding process itself, before the video is fully compressed and stored. The encoder analyzes macroblock types and motion vector distributions as they are generated during compression, allowing scene change identification to occur in advance of playback without requiring separate decoding operations. This resolves the contradiction by obtaining accurate scene change information through preliminary analysis during encoding, avoiding the need for computationally expensive post-encoding decoding operations.
2Productivity
If conventional methods use motion vector analysis during encoding to identify scene changes, then processing speed is improved, but false scene change identification increases due to gradual image content changes
Solution Approach 1:
The patent applies parameter changes by monitoring the distribution patterns of macroblock types (intra-coded vs. inter-coded) and motion vector magnitudes across multiple frames. Instead of relying on a single threshold, the system tracks how these parameters change over time and identifies scene changes based on significant statistical deviations in the macroblock type distribution. This resolves the contradiction by using multiple correlated parameters with temporal analysis to maintain high processing speed while reducing false positives from gradual content changes.
Solution Approach 2:
The patent implements feedback by continuously monitoring the distribution of macroblock types and comparing it against reference distributions from previous frames. The system uses this feedback to dynamically adjust its scene change detection thresholds and to distinguish between gradual content evolution (where macroblock distributions change slowly) and actual scene changes (where distributions change abruptly). This feedback mechanism maintains processing speed while significantly improving identification reliability.
3Stability of the object's composition
If conventional encoders maintain fixed bit rates, then encoding consistency is improved, but image quality at scene changes deteriorates
Solution Approach 1:
The patent applies dynamics by introducing dynamic bit rate adjustment based on detected scene changes. The encoder operates with a base target bit rate for normal operation but dynamically increases the allocated bits for frames identified as scene changes. This allows the system to maintain encoding consistency during standard operation while temporarily enhancing image quality at critical scene transition points, resolving the contradiction between stability and precision through conditional dynamic adjustment.
Data Source
AI summary
A video encoder identifies scene changes during the encoding of video sequences based on the distribution of macroblock types within each image. Once an image is identified as a scene change, control information is sent to a quantizer to adjust the quantization of the image to improve image quality. The image quantization is improved without changing the frame or picture type of the image within its group of pictures. To improve the image quality, the quantizer can directly or indirectly change the quantization parameter used to quantize the DCT coefficients for the image.