Feature-Based Video Partitioning for Spatial Scalability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video compression techniques, such as MPEG-2 and MPEG-4 Part 10/AVC, face limitations in adapting block size to content, leading to inefficiencies in coding and visual perception due to blocking artifacts.
Innovation Solution
A feature-based coding method that divides video streams into partitions based on specific feature sets, using optimized encoding schemes for each partition to improve coding efficiency and visual quality, allowing for adaptive spatial and temporal prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If block-based motion compensated transform coding is used with fixed block sizes, then coding efficiency can be achieved through standardized processing, but blocking artifacts occur and visual perception is degraded
Solution Approach 1:
The video stream is divided into multiple partitions based on detected features (edges, textures, motion regions). Each partition is then encoded with appropriate block sizes adapted to its local content characteristics, rather than applying a uniform block size across the entire frame. This segmentation approach allows the coding system to handle different regions with different block sizes, improving visual quality while maintaining coding efficiency.
Solution Approach 2:
Different block sizes and coding parameters are applied to different partitions of the video stream based on local content features. Smooth regions use larger blocks for efficient compression, while regions with edges or textures use smaller blocks to avoid blocking artifacts. This local adaptation of coding quality to match local image characteristics resolves the contradiction between overall coding efficiency and local visual perception.
2Object-affected harmful factors
If adaptive block size is used to reduce blocking artifacts, then visual perception is improved, but device complexity increases due to multiple block size configurations
Solution Approach 1:
The block size configuration is made dynamic and adaptive rather than static. The system automatically detects local image features (edges, textures, motion) and dynamically selects appropriate block sizes for each partition. This dynamic adaptation allows the system to reduce blocking artifacts where needed while maintaining simplicity in uniform regions, balancing visual quality improvement with manageable device complexity.
Solution Approach 2:
The patent changes the parameter of block size from a fixed value to a variable parameter that is adjusted based on local content characteristics. By detecting features such as edge density, texture complexity, and motion magnitude, the system modifies block size parameters locally to optimize visual quality. This parameter adaptation approach reduces blocking artifacts without requiring completely complex system redesign, as it builds upon existing block-based coding frameworks.
3Productivity
If feature-based partitioning is applied to adapt coding to local content, then visual perception and coding efficiency are improved, but processing complexity increases
Solution Approach 1:
The system performs preliminary feature detection and partitioning before the actual encoding process. By analyzing edges, textures, and motion regions in advance and creating a partition map, the system prepares the video stream for adaptive block-based encoding. This preliminary action separates the complex feature analysis from the encoding process, allowing efficient coding to follow once the partitions are established, thus improving overall coding efficiency while managing processing complexity through staged processing.
Solution Approach 2:
The patent introduces an intermediary partitioning structure that acts as a mediator between the raw video stream and the encoding process. Feature detection algorithms analyze the video content and create partition boundaries based on local characteristics. This intermediary partition map guides subsequent encoding operations, allowing the system to adapt to local content without requiring complex real-time adjustments during encoding. The intermediary structure simplifies the overall process by pre-organizing the video data according to its inherent features.
Data Source
AI summary
In a video distribution system, a divider to segment an input video stream into partitions for each of a plurality of channels of the video stream is provided. A channel analyzer is coupled to the divider wherein the channel analyzer decomposes the partitions. An encoder is coupled to the channel analyzer to encode the decomposed partitions into an encoded bitstream wherein the encoder receives coding information from at least one of the plurality of channels to be used in encoding the decomposed partitions into the encoded bitstream. A decoder receives the coded bitstream to decode the received bitstream and to reconstruct the input video stream. The decoder uses the coding information to decode the bitstream.