Image Encoding Block Segmentation for Rotation and Cropping
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Solution Overview
Problem
Existing image encoding and decoding methods face challenges in efficiently compressing images while maintaining quality, especially when rotating or cropping images, due to limitations in block structure and synchronization points, leading to potential loss of information and increased memory requirements.
Innovation Solution
The method involves dividing images into blocks of varying sizes, allowing for block-by-block encoding and decoding, with synchronization points and predictors tailored to each block, enabling efficient compression and rotation/cropping without waiting for complete pixel information, and supporting multiple encoding/decoding methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If images are divided into fixed-size blocks for encoding, then the encoding process becomes systematic and manageable, but it becomes difficult to handle image rotations and cropping operations efficiently
Solution Approach 1:
The image is divided into multiple blocks of pixels, where each block can be independently encoded and decoded. This segmentation allows the encoding system to handle different operations (rotation, cropping) on individual blocks without requiring complete image processing, thus improving adaptability while maintaining systematic organization
Solution Approach 2:
The patent introduces synchronization points that can be dynamically positioned within blocks based on operation requirements. Predictors are also adapted dynamically to work with the specific block structure and synchronization point locations, enabling flexible handling of rotation and cropping operations
2Measurement precision
If complete pixel information is required before decoding, then decoding accuracy is maintained, but transmission time and memory requirements increase
Solution Approach 1:
The image decoding process is segmented into independent block-level operations. Each block can be decoded separately using its own synchronization points and predictors, allowing parallel processing and early termination without requiring complete transmission of all pixel information, thus reducing time loss while maintaining accuracy
Solution Approach 2:
Synchronization points are pre-established within each block to provide reference information needed for accurate decoding. This preliminary structuring of reference data allows decoders to achieve accurate reconstruction without waiting for complete pixel information, as the synchronization points provide sufficient context for independent block decoding
3Stability of the object's composition
If uniform block structures are used across the image, then encoding consistency is maintained, but memory requirements and processing complexity increase for varied operations
Solution Approach 1:
The image is divided into multiple independent blocks that can have varying characteristics. This segmentation allows different blocks to be optimized for different operations (some blocks may be rotated, others cropped, some encoded with different predictors), reducing overall system complexity while maintaining encoding consistency through standardized block interfaces
Solution Approach 2:
Different blocks can have locally optimized properties such as different synchronization point positions, different predictor configurations, and different encoding parameters tailored to their specific content and operation requirements. This local customization reduces processing complexity and memory requirements compared to uniform processing of the entire image
Data Source
AI summary
A method as well as a system, a device, an encoding apparatus, a decoding apparatus, a module and a computer software product for image processing is disclosed. The image comprises a pixel matrix, in which the pixels comprise a first number of bits. The pixel matrix is divided to two or more blocks of pixels. The pixels are processed on a block-by-block basis to form encoded pixel values including a certain second number of bits. Bit strings are formed on the basis of the encoded pixels. When decoding the image the bit strings are examined to find out the encoding method used in encoding the pixel, and decoding is performed on a block-by-block basis to retrieve pixel values of the image.