Perceptive Vectorial Quantization for Digital Video Signal Compression
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Solution Overview
Problem
Existing digital image compression techniques are computationally complex and difficult to integrate into System on a Chip (SoC) solutions, requiring high processing operations and storage units, which increases production costs and power dissipation, especially in low-power devices.
Innovation Solution
The implementation of a vector or multi-dimensional quantizer with non-uniform quantization cells for digital-data arrays, specifically designed to reduce both statistical and perceptive redundancy, minimizing encoding and decoding complexity and enabling efficient image compression and decompression, which can be integrated into digital units and image-display systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional digital image compression techniques are used, then compression efficiency is improved, but computational complexity and device complexity increase
Solution Approach 1:
The patent divides the image data into blocks and applies separate quantization to luminance and chrominance components. The luminance block is processed independently from chrominance blocks, allowing simplified processing of each segment while maintaining overall compression efficiency.
Solution Approach 2:
The patent applies different quantization strategies to different components: luminance receives uniform quantization while chrominance receives non-uniform quantization. This local differentiation optimizes compression for each component's perceptual importance without requiring complex global processing.
2Productivity
If traditional digital image compression techniques are used, then compression efficiency is improved, but area occupied by compression circuits increases
Solution Approach 1:
The patent extracts and processes only the essential components (luminance and chrominance blocks) separately, eliminating the need for complex full-image processing circuits. This reduction to essential elements decreases circuit area while preserving compression effectiveness.
Solution Approach 2:
The patent changes the quantization parameter uniformly for luminance and non-uniformly for chrominance, simplifying the circuit design by using different but manageable parameter sets for different components rather than complex adaptive parameters.
3Productivity
If traditional digital image compression techniques are used, then compression efficiency is improved, but power dissipation increases
Solution Approach 1:
By segmenting the processing into separate luminance and chrominance blocks with different quantization methods, the patent reduces the computational workload in each processing unit, thereby lowering power consumption while maintaining compression efficiency.
Solution Approach 2:
The use of fixed uniform quantization for luminance and fixed non-uniform quantization for chrominance eliminates the need for complex adaptive parameter calculations, reducing computational operations and power dissipation.
4Quantity of substance
If chrominance is under-sampled, then bit rate is reduced, but image quality perception deteriorates
Solution Approach 1:
The patent applies non-uniform quantization to chrominance blocks, concentrating quantization levels where the human visual system is more sensitive and using coarser quantization where sensitivity is lower. This maintains perceptual quality while achieving bit rate reduction through intelligent parameter distribution.
Data Source
AI summary
The system carries out conversion of digital video signals organized in blocks of pixels from a first format to a second format. The second format is a format compressed via vector quantization. The vector quantization is performed by means of repeated application of a scalar quantizer to the pixels of said blocks with a quantization step (Q) determined in an adaptive way according to the characteristics of sharpness and/or brightness of the pixels.