Adaptive Block Transform Encoding for Lossless Image Compression
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Solution Overview
Problem
Conventional codecs fail to efficiently encode extensive areas with constant parameters while maintaining high spatial detail, often resulting in increased data size due to inefficiencies in block sizing and error correction, leading to suboptimal data compression for images and videos.
Innovation Solution
A method that processes input data into variable-sized blocks, applies multiple transformations, checks quality criteria, and adjusts block sizes or combines blocks to ensure efficient encoding with minimal loss of quality, using a combination of coding methods like DCT, wavelet transforms, and entropy coding to achieve lossless compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional codecs use fixed block sizes for encoding, then encoding process is simple, but encoding efficiency is poor for areas with different spatial details
Solution Approach 1:
The image is divided into multiple blocks of different sizes based on spatial detail requirements. Areas with high spatial detail use smaller blocks while extensive areas with constant parameters use larger blocks, optimizing encoding efficiency for different regions
Solution Approach 2:
Different block sizes are applied to different regions of the image based on local characteristics. High-detail areas receive finer block segmentation while uniform areas use coarser blocks, achieving local optimization of encoding quality and efficiency
2Manufacturing precision
If smaller block sizes are used for higher encoded image quality, then image quality is improved, but data compression ratio decreases
Solution Approach 1:
Small blocks are applied only to regions requiring high spatial detail representation, while large blocks are used for extensive areas with constant parameters. This local differentiation maintains image quality where needed while minimizing overall data size
Solution Approach 2:
The image is segmented into regions of different complexity, with block size selection optimized for each segment. This prevents unnecessary use of small blocks in uniform areas, improving compression ratio while maintaining quality in critical regions
3Adaptability or versatility
If multiple different block sizes are used for encoding, then encoding adaptability is improved, but encoding process complexity increases
Solution Approach 1:
The block size parameter is dynamically changed based on image content analysis. The encoder adapts block sizes to match local image characteristics, achieving high encoding adaptability through systematic parameter variation
Data Source
AI summary
An encoder includes data processing hardware operable to: process input data into a plurality of blocks/packets; apply a plurality of transformations to content of the blocks/packets to generate corresponding transformed data; check a quality of representation of the transformed data prior to application of the transformations to determine whether or not the quality of representation of the transformed data satisfies quality criteria; if the quality of representation does not satisfy the quality criteria, to divide and/or combine the one or more individual blocks or packets further and repeating the transformation step; and if the quality of representation of the transformed data satisfies the one or more quality criteria, to select coding methods and encode data representative of the input data to be encoded to provide encoded output data; and communicate in the encoded data information describing the plurality of transformations or combinations of transformations employed when coding the blocks/packets.