Adaptive Video Encoding by Image Category Classification
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Solution Overview
Problem
Conventional video encoding/decoding apparatuses apply fixed motion prediction and transformation schemes to all images, inefficiently handling images with different attributes, such as natural images and those containing text or graphics, leading to suboptimal encoding and decoding performance.
Innovation Solution
A video encoding/decoding apparatus that classifies input images into categories based on attributes like directivity, edge component distribution, and color format, applying distinct encoding and decoding schemes for each category, including adaptive transforms, quantizations, and predictions tailored to specific image types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed motion prediction and transformation schemes are applied to all images, then device complexity is reduced and ease of operation is improved, but encoding efficiency deteriorates and information loss increases for images with different attributes
Solution Approach 1:
The patent segments the encoding process by dividing images into different categories (natural images, text/graphics images, etc.) based on attribute analysis. Each category receives tailored encoding schemes, allowing the system to optimize for specific image types without uniformly increasing complexity across all operations.
Solution Approach 2:
The patent introduces dynamic adaptability by analyzing image attributes and selectively applying different motion prediction and transformation schemes based on the identified category. This dynamic approach allows the encoding system to adjust its behavior to match the specific characteristics of each image type, improving efficiency without requiring all possible schemes to be simultaneously implemented.
2Productivity
If fixed encoding schemes are applied to all images, then ease of operation is improved, but encoding efficiency and information preservation deteriorate for specific image categories
Solution Approach 1:
The patent applies local quality by tailoring the encoding scheme to the specific characteristics of each image category. Natural images receive different treatment than text or graphics images, ensuring that each category's unique features are preserved appropriately. This localized approach to quality encoding reduces information loss for each specific type while maintaining overall system efficiency.
3Reliability
If category-based classification and tailored encoding schemes are applied, then encoding efficiency is improved and information loss is reduced, but device complexity increases
Solution Approach 1:
The patent performs preliminary action by analyzing and classifying image attributes before the actual encoding process. This pre-classification step enables the system to select the appropriate encoding scheme in advance, ensuring high decoding accuracy for each category while managing complexity by making the classification decision once rather than repeatedly during encoding.
4Productivity
If category-based classification and tailored encoding schemes are applied, then encoding efficiency is improved, but device complexity and operational complexity increase
Solution Approach 1:
The patent implements self-service by enabling the encoding system to automatically analyze image attributes and select appropriate encoding schemes without requiring manual intervention. The system serves itself by making intelligent decisions about which category each image belongs to and which encoding parameters to apply, maintaining ease of operation while achieving category-optimized encoding efficiency.
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.