Spatial Tile Image Encoding for Progressive Quality Decoding
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Solution Overview
Problem
Existing digital image compression methods require parsing the entire bitstream to reconstruct an image, which is inefficient and limits progressive decoding and spatially selective transmission.
Innovation Solution
A flexible encoded format that allows images to be parsed into separate bitstream units, enabling progressive decoding, spatially selective transmission, and spatially variable encoding quality levels by generating a log of coding quality levels and using independent or derived-type tile-layer items for encoding and decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire bitstream is parsed to reconstruct an image, then complete image reconstruction is achieved, but decoding efficiency is reduced and progressive decoding is limited
Solution Approach 1:
The image is divided into multiple independent tiles, each encoded separately with its own bitstream. This segmentation allows the decoder to process and reconstruct individual tiles independently without parsing the entire bitstream, enabling progressive decoding where tiles can be decoded in any order and at different quality levels simultaneously
Solution Approach 2:
The encoder generates multiple quality levels for each tile (excessive action), allowing the decoder to select and decode only the required quality level (partial action). This enables progressive decoding where lower quality levels can be decoded first, with optional refinement to higher quality levels later, improving decoding efficiency by avoiding unnecessary processing of higher quality data when not needed
2Productivity
If spatial portions are transmitted selectively, then transmission efficiency is improved, but the encoding format must support independent parsing of spatial regions
Solution Approach 1:
The image is segmented into independent tiles that can be transmitted separately. Each tile contains complete encoding information including quality level logs, allowing selective transmission of specific tiles based on spatial importance without requiring the entire image data, thus improving transmission efficiency
Solution Approach 2:
The encoding format adds a quality level dimension to each tile, creating a multi-layered structure where tiles can be transmitted at different quality levels. This dimensional addition enables flexible selective transmission where both spatial selection (which tiles) and quality selection (at what level) can be independently controlled
3Productivity
If different spatial regions are encoded at different quality levels, then coding efficiency is improved, but the encoding process becomes more complex
Solution Approach 1:
Different quality levels are applied to different spatial tiles based on their importance. Important regions (e.g., containing key subjects) are encoded at higher quality levels while less important regions use lower quality levels, optimizing overall coding efficiency by allocating bits according to spatial importance
Solution Approach 2:
The encoding process varies the quality level parameter across different tiles. Each tile can have its quality level independently configured, allowing the encoder to adaptively adjust compression parameters based on local image content characteristics and importance, improving coding efficiency through parameter optimization
Data Source
AI summary
Improved still image encoding techniques may include generating first items of a log and second items of coded tile-layer image data, where the log enumerates coding quality levels contained in the second items. The second items may include independent-type items, derived-type items of identity variant, and derived-type items with other variant(s). Such encoding techniques may provide for progressive decoding of images, and may provide for spatially variable encoding quality levels.