Adaptable Golomb Coding for Image Compression
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Solution Overview
Problem
Current image and video compression techniques face challenges in balancing compression ratio and image quality, with lossless compression offering high fidelity but low ratios and lossy compression sacrificing detail for efficiency, often requiring a trade-off between the two.
Innovation Solution
Implementing a hybrid codec that selectively uses lossless compression for detailed areas and lossy compression for non-detailed areas, along with a lossy block repair process to improve image quality by re-encoding unchanged blocks using lossless compression, and employing adaptive encoding schemes with smaller lookup tables for efficient compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lossless compression is used, then image quality is maintained, but compression ratio is low
Solution Approach 1:
The patent divides the image into multiple blocks and applies different compression strategies to different blocks based on their characteristics. Important blocks (containing edges, textures, or significant features) are compressed using lossless methods to preserve quality, while less important blocks are compressed using lossy methods to improve overall compression ratio. This local differentiation resolves the contradiction by maintaining quality where needed while achieving compression elsewhere.
Solution Approach 2:
The image is segmented into multiple blocks that can be independently processed. By segmenting the image, the system can apply lossless compression only to specific blocks that require high fidelity, while applying more aggressive compression to other blocks, thus achieving both high image quality and good compression ratio simultaneously.
2Productivity
If lossy compression is used, then compression ratio is improved, but image quality deteriorates
Solution Approach 1:
The system applies lossy compression selectively only to blocks that do not contain important visual features, while preserving lossless compression for blocks with edges, textures, or significant content. This localized application of lossy compression improves overall compression ratio without noticeably deteriorating perceived image quality.
Solution Approach 2:
The patent introduces an intermediate analysis step that evaluates each block's importance and characteristics before selecting the compression method. This intermediary process acts as a mediator between lossless and lossy compression, determining the optimal approach for each block to balance compression ratio and image quality.
3Adaptability or versatility
If JPEG compression is used, then both lossless and lossy modes are supported, but not simultaneously for the same data
Solution Approach 1:
The patent segments the image into multiple blocks and processes each block independently with different compression modes. This segmentation allows the system to simultaneously apply both lossless and lossy compression to different portions of the same image data, overcoming the limitation of traditional JPEG that requires choosing a single mode for the entire image.
Solution Approach 2:
The system dynamically selects the compression mode for each block based on its characteristics, rather than using a static global mode. This dynamic adaptation allows simultaneous use of both lossless and lossy compression within the same image, increasing versatility while managing complexity through automated block-level decision making.
Data Source
AI summary
Systems and methods are described herein for encoding and decoding image data. In one aspect, pixel data of a frame of image data may be obtained, where the pixel data contains multiple values. The pixels values may be encoded by determining a length of a first value, where the length is determined using a number of bits of the value for a first type of color value and a modified number of bits for a second type of color value. Both of the number of bits and the modified number of bits map to a first color type lookup table. A code length of the value may be determined using the first color type lookup table, wherein the first color type lookup table maps a plurality of lengths to a plurality of code lengths. A code may be generated for the value based on the value and the code length.