Image Encoding Apparatus Using Category-Based Intra Prediction
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Solution Overview
Problem
Conventional video encoding/decoding apparatuses apply fixed motion prediction and compensation, transform, quantization, and encoding/decoding schemes to all input images, which is inefficient as each image has unique attributes and characteristics.
Innovation Solution
A video encoding/decoding apparatus that classifies input images into two or more categories based on preset attributes and applies different encoding/decoding schemes to each category, optimizing processes such as intra-prediction, inter-prediction, transform, and quantization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed encoding/decoding schemes are applied to all input images, then device complexity is reduced and ease of operation is improved, but encoding efficiency deteriorates and information loss increases
Solution Approach 1:
The patent segments input images into different categories (e.g., natural images, screen content, depth maps) based on image characteristics. Each category is then processed using optimized encoding/decoding schemes tailored to its specific properties, thereby improving encoding efficiency without requiring complete system redesign
Solution Approach 2:
The patent introduces dynamic scheme selection where the encoding/decoding approach adapts based on the detected image category. The system dynamically switches between different processing schemes (e.g., transform types, prediction methods, quantization parameters) according to the specific image being processed, optimizing performance for each case
2Loss of information
If fixed encoding/decoding schemes are applied to all input images, then ease of operation is improved, but loss of information increases
Solution Approach 1:
The patent applies different encoding/decoding schemes to different image categories based on their specific characteristics. For example, natural images use one set of processing parameters while screen content uses another, ensuring each category receives locally optimized treatment that preserves its specific information characteristics
Solution Approach 2:
The patent changes key processing parameters (transform type, prediction mode, quantization strength) based on the detected image category. This allows the system to optimize information preservation for each category by selecting parameters suited to their specific properties rather than using fixed parameters for all images
Data Source
AI summary
According to the present invention, an adaptive scheme is applied to an image encoding apparatus that includes an inter-predictor, an intra-predictor, a transformer, a quantizer, an inverse quantizer, and an inverse transformer, wherein input images are classified into two or more different categories, and two or more modules from among the inter-predictor, the intra-predictor, the transformer, the quantizer, and the inverse quantizer are implemented to perform respective operations in different schemes according to the category to which an input image belongs. Thus, the invention has the advantage of efficiently encoding an image without the loss of important information as compared to a conventional image encoding apparatus which adopts a packaged scheme.