Encoder Assisted Frame Rate Up Conversion with Motion Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current Frame Rate Up Conversion (FRUC) techniques struggle to accurately model complex motion in video frames, leading to low-quality interpolated frames and inefficient bandwidth usage, particularly in mobile devices with low computational power.
Innovation Solution
The Encoder Assisted Frame Rate Up Conversion (EA-FRUC) system employs various motion models, including affine motion models, to improve the modeling of moving objects and reduce bandwidth requirements by transmitting additional information from the encoder to the decoder for more accurate interpolation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional motion compensated interpolation is used for frame rate up conversion, then the processing can be implemented at the decoder, but the interpolated frames suffer from block artifacts and poor quality due to inaccurate motion vectors
Solution Approach 1:
The encoder performs motion estimation and generates motion vectors in advance during the encoding process. These pre-computed motion vectors are transmitted to the decoder, eliminating the need for the decoder to perform complex motion estimation. This preliminary action at the encoder resolves the contradiction by providing accurate motion information before decoding, improving interpolated frame quality without increasing decoder complexity.
Solution Approach 2:
The patent introduces motion vectors as an intermediary element that bridges the encoder and decoder. Instead of the decoder directly performing motion estimation on raw frames, it uses the transmitted motion vectors to guide the interpolation process. This intermediary provides accurate motion information to the decoder, resolving the quality-precision issue without burdening the decoder with complex computation.
2Loss of energy
If frame rates are dropped to reduce bandwidth for mobile devices, then bandwidth requirements are reduced, but video quality suffers from jerkiness and motion tracking errors
Solution Approach 1:
The system uses motion estimation feedback to dynamically determine which frames to drop and which to keep. By analyzing motion characteristics between frames, the encoder makes intelligent decisions about frame retention, ensuring that frames with significant motion changes are preserved while frames with minimal motion can be dropped. This feedback mechanism maintains video quality smoothness while reducing bandwidth consumption.
Solution Approach 2:
The patent changes the temporal sampling parameter (frame rate) dynamically based on motion characteristics. Instead of uniformly dropping frames at a fixed rate, the system adjusts frame retention based on motion magnitude and complexity. This parameter change allows bandwidth reduction while maintaining quality by preserving frames during high-motion segments and dropping frames during low-motion segments.
3Measurement precision
If encoder assisted frame rate up conversion with multiple motion models is implemented, then interpolation quality is improved, but encoding complexity increases
Solution Approach 1:
The patent segments the video frame into multiple regions or blocks, each potentially requiring different motion models. By dividing the frame into segments, the encoder can apply appropriate motion compensation techniques to each segment independently, improving overall interpolation accuracy. This segmentation approach manages encoding complexity by localizing complex processing to only where needed rather than applying it globally.
Solution Approach 2:
The system dynamically selects and applies different motion models based on the characteristics of each video region or sequence. Rather than using a single fixed motion model, the encoder adapts its approach based on local motion patterns, making the encoding process dynamic and efficient. This dynamic adaptation improves interpolation accuracy while managing complexity by applying complex models only where necessary.
Data Source
AI summary
An Encoder Assisted Frame Rate Up Conversion (EA-FRUC) system that utilizes various motion models, such as affine models, in addition to video coding and pre-processing operations at the video encoder to exploit the FRUC processing that will occur in the decoder in order to improve the modeling of moving objects, compression efficiency and reconstructed video quality. Furthermore, objects are identified in a way that reduces the amount of information necessary for encoding to render the objects on the decoder device.