Hierarchical Image Compression with Progressive Decoding
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Solution Overview
Problem
Current image compression techniques face challenges in achieving a balance between compression ratio and image quality, particularly in handling signal loss and network bandwidth fluctuations, while also being resource-efficient and resilient to noise.
Innovation Solution
A method involving recursive hierarchical encoding and decoding of images, transforming initial two-dimensional pixel arrays into progressively decremented arrays of signs and one-dimensional values, allowing for efficient storage and reconstruction with a reasonable compression ratio and resilient lossy representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If lossy compression is used to reduce image size, then storage space and bandwidth are reduced, but image quality deteriorates due to loss of fine details
Solution Approach 1:
The image is divided into multiple resolutions or quality levels, allowing selective transmission or storage of different segments. This enables progressive decoding where lower-resolution versions can be displayed first, then progressively enhanced with higher-quality data as bandwidth or storage becomes available, resolving the contradiction between compressed size and image quality.
Solution Approach 2:
The patent applies transform coding (such as DCT or wavelet transforms) to convert image data from spatial domain to frequency domain, then quantizes the transformed coefficients. By adjusting quantization parameters, the system can control the trade-off between compression ratio and perceived image quality, preserving visually important information while discarding less significant details.
2Productivity
If high compression ratio is achieved through lossy compression, then storage and transmission efficiency improve, but resilience to signal loss and corruption deteriorates
Solution Approach 1:
The patent implements progressive encoding where image data is organized in nested layers of increasing quality or resolution. Each layer contains information that enhances the previous layers, allowing the image to be reconstructed at multiple quality levels. This nested structure provides resilience because lower-quality versions remain valid even if higher-quality data is lost or corrupted during transmission.
Solution Approach 2:
The encoding scheme incorporates error resilience by distributing information across multiple layers and using transform coding that concentrates energy in fewer coefficients. This beforehand cushioning ensures that even if some data is corrupted, the fundamental image structure remains intact and can be progressively enhanced rather than completely degraded.
3Ease of manufacture
If conventional compression techniques are used, then implementation is simple, but adaptability to different bandwidth conditions and quality requirements is limited
Solution Approach 1:
The patent implements dynamic adaptability where the encoder can adjust compression parameters, quality levels, and resolution based on available bandwidth, storage constraints, and device capabilities. The progressive and layered structure allows the system to dynamically select which layers to transmit or store, providing versatility across different operating conditions while maintaining a relatively simple base encoding framework.
Data Source
AI summary
An image processing method includes selecting an image in fixed storage of a computer and loading the selected image into memory of the computer. The method further includes representing the loaded image by a processor of the computer in the memory as an initial two-dimensional array of pixel values. Thereafter, the initial two-dimensional array of pixel values may be transformed into a hierarchy of progressively axially decremented two-dimensional arrays of signs and a pair of one-dimensional values for each 2×2 array of signs amongst the decremented two-dimensional arrays of signs. Finally, each of the two-dimensional arrays of signs and each pair of one-dimensional values may be stored in the fixed storage as a compressed form of the selected image.