Block Map Image Decoding for Adaptive Neighbor Reference
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Solution Overview
Problem
Existing image encoding and decoding methods struggle to efficiently handle high-resolution or high-quality image content by optimally determining data units for compression, leading to suboptimal compression ratios and decoding efficiency.
Innovation Solution
The use of a block map to determine decoded and non-decoded blocks, allowing for adaptive reference to neighboring regions during encoding and decoding processes, including square and non-square shapes, and updating the block map as blocks are decoded.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If image data is encoded using conventional compression standards with fixed data unit sizes, then encoding and decoding processes are simple, but compression ratios are suboptimal for high-resolution images
Solution Approach 1:
The image is divided into multiple data units of different sizes (e.g., 64x64, 32x32, 16x16 blocks) rather than using a fixed size. This segmentation allows the encoder to adaptively select appropriate block sizes for different regions of the image, improving compression efficiency while managing complexity through hierarchical processing
Solution Approach 2:
The data unit size is made dynamic rather than fixed. The encoding system adaptively determines the optimal data unit size for each region based on image characteristics such as complexity, texture, and frequency content, allowing the compression ratio to be improved without requiring overly complex manual configuration
2Speed
If larger data units are used for compression, then processing speed increases, but decoding accuracy and image quality deteriorate
Solution Approach 1:
The image is segmented into multiple data units of varying sizes, allowing larger blocks for simple regions (faster processing) and smaller blocks for complex regions (higher quality). This hierarchical segmentation enables the system to achieve both speed and quality by processing different regions with appropriately sized units
Solution Approach 2:
Different data unit sizes are applied to different regions of the image based on local characteristics. Simple regions use larger data units for faster decoding, while complex regions use smaller data units for higher quality, thereby achieving both speed and accuracy requirements simultaneously
3Productivity
If the image is divided into more blocks, then compression efficiency improves, but the complexity of determining reference regions for decoding increases
Solution Approach 1:
A block map is constructed in advance during the encoding process to record the spatial relationships and decoding orders of all blocks. This preliminary action stores reference information that simplifies the decoding process, allowing the decoder to efficiently determine reference regions without re-analyzing complex spatial relationships during decoding
Solution Approach 2:
The block map serves as an intermediary data structure that mediates between the encoded bitstream and the decoding process. It pre-organizes block information including positions, sizes, and reference relationships, thereby reducing the complexity of reference region determination during actual decoding while maintaining high compression efficiency
Data Source
AI summary
Provided are a method and device for encoding or decoding an image by using a block map. In the method and device for decoding an image according to an embodiment set forth herein, a bitstream of an encoded image is received, one or more blocks in a picture are determined using split information obtained from the bitstream, a block map indicating whether or not a previous block among the one or more blocks has been decoded is determined, a neighboring region which is available to be referred to for a current block to be decoded in a certain scan order among the one or more blocks is determined, and the current block is decoded based on the neighboring region which is available to be referred to.