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Found 11 papers, 0 papers with code
NERV++: An Enhanced Implicit Neural Video Representation
Neural fields, also known as implicit neural representations (INRs), have shown a remarkable capability of representing, generating, and manipulating various data types, allowing for continuous data reconstruction at a low memory footprint.
Bitrate Ladder Prediction Methods for Adaptive Video Streaming: A Review and Benchmark
A key component of HAS is the bitrate ladder, which provides the encoding parameters (e. g., bitrate-resolution pairs) to encode the source video.
ConvNeXt-ChARM: ConvNeXt-based Transform for Efficient Neural Image Compression
Over the last few years, neural image compression has gained wide attention from research and industry, yielding promising end-to-end deep neural codecs outperforming their conventional counterparts in rate-distortion performance.
AICT: An Adaptive Image Compression Transformer
Motivated by the efficiency investigation of the Tranformer-based transform coding framework, namely SwinT-ChARM, we propose to enhance the latter, as first, with a more straightforward yet effective Tranformer-based channel-wise auto-regressive prior model, resulting in an absolute image compression transformer (ICT).
Joint Hierarchical Priors and Adaptive Spatial Resolution for Efficient Neural Image Compression
Recently, the performance of neural image compression (NIC) has steadily improved thanks to the last line of study, reaching or outperforming state-of-the-art conventional codecs.
Ensemble Learning for Efficient VVC Bitrate Ladder Prediction
When compared to static bitrate ladder, the proposed method can offer about 13% bitrate reduction in terms of BD-BR with a negligible additional computational overhead.
Prediction-Aware Quality Enhancement of VVC Using CNN
The upcoming video coding standard, Versatile Video Coding (VVC), has shown great improvement compared to its predecessor, High Efficiency Video Coding (HEVC), in terms of bitrate saving.
A CNN-based Prediction-Aware Quality Enhancement Framework for VVC
This paper presents a framework for Convolutional Neural Network (CNN)-based quality enhancement task, by taking advantage of coding information in the compressed video signal.
Model Selection CNN-based VVC QualityEnhancement
Moreover, an optional Model Selection (MS) strategy is adopted to pick the best trained model among available ones at the encoder side and signal it to the decoder side.
Quality-driven Variable Frame-Rate for Green Video Coding in Broadcast Applications
In this paper, we propose a Variable Frame Rate (VFR) solution to determine the minimum (critical) frame-rate that preserves the perceived video quality of HFR video.
Quality Assessment of DIBR-synthesized views: An Overview
In this paper, we provide a comprehensive survey on various current approaches for DIBR-synthesized views.
