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- NTIRE 2024 Quality Assessment of AI-Generated Content Challenge54
This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC).
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- The Tenth NTIRE 2025 Efficient Super-Resolution Challenge Report39
This paper presents a comprehensive review of the NTIRE 2025 Challenge on Single-Image Efficient Super-Resolution, emphasizing groundbreaking advancements in state-of-the-art single-image ESR techniques.
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- Skip-Concatenated Image Super-Resolution Network for Mobile Devices15
This study proposed an efficient and lightweight super-resolution network that enables real-time performance on mobile devices and introduced a skip connection layer by directly concatenating a low-resolution input image with an intermediate feature map to reduce the quantization errors commonly encountered during float-to-int8 model conversion.
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- Sparse Recovery Using Sparse Sensing Matrix Based Finite Field Optimization in Network Coding5
This paper proposes a sparse recovery approach using sparse sensing matrix to solve the NC all-or-nothing problem over a sensor network.
- A Real-Time Video Stitching Algorithm in H.264/AVC Compressed Domain5
A new compressed domain combiner that works in boundary macroblocks of input videos with re-calculating intra prediction mode, intra prediction MVD, a re-allocation of the coefficient table, and border extension methods is presented.
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- RCV2023 Challenges: Benchmarking Model Training and Inference for Resource-Constrained Deep Learning3
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- Compression-friendly Image Encryption Algorithm Based on Order Relation1
An image encryption algorithm that can be used in combination with compression algorithms that significantly increases PSNR and SSIM values and improves compression efficiency while ensuring security.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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