44 citations · 164 across the 35 of their papers we have counts for
23 papers · 1 filter
A Multi-Grid Implicit Neural Representation for Multi-View Videos
Qingyue Ling, Zhengxue Cheng, Donghui Feng +6
Multi-view videos are becoming widely used in different fields, but their high resolution and multi-camera shooting raise significant challenges for storage and transmission. In th…
LMM-driven Semantic Image-Text Coding for Ultra Low-bitrate Learned Image Compression
Shimon Murai, Heming Sun, Jiro Katto
Supported by powerful generative models, low-bitrate learned image compression (LIC) models utilizing perceptual metrics have become feasible. Some of the most advanced models achi…
Attack and Defense Analysis of Learned Image Compression
Tianyu Zhu, Heming Sun, Xiankui Xiong +4
Learned image compression (LIC) is becoming more and more popular these years with its high efficiency and outstanding compression quality. Still, the practicality against modified…
Learned Image Compression with Mixed Transformer-CNN Architectures
Jinming Liu, Heming Sun, Jiro Katto
Learned image compression (LIC) methods have exhibited promising progress and superior rate-distortion performance compared with classical image compression standards. Most existin…
Learned Lossless Image Compression With Combined Autoregressive Models And Attention Modules
Ran Wang, Jinming Liu, Heming Sun +1
Lossless image compression is an essential research field in image compression. Recently, learning-based image compression methods achieved impressive performance compared with tra…
Streaming-capable High-performance Architecture of Learned Image Compression Codecs
Fangzheng Lin, Heming Sun, Jiro Katto
Learned image compression allows achieving state-of-the-art accuracy and compression ratios, but their relatively slow runtime performance limits their usage. While previous attemp…
Memory-Efficient Learned Image Compression with Pruned Hyperprior Module
Ao Luo, Heming Sun, Jinming Liu +1
Learned Image Compression (LIC) gradually became more and more famous in these years. The hyperprior-module-based LIC models have achieved remarkable rate-distortion performance. H…
Q-LIC: Quantizing Learned Image Compression with Channel Splitting
Heming Sun, Lu Yu, Jiro Katto
Learned image compression (LIC) has reached a comparable coding gain with traditional hand-crafted methods such as VVC intra. However, the large network complexity prohibits the us…
End-to-End Learned Image Compression with Quantized Weights and Activations
Heming Sun, Lu Yu, Jiro Katto
End-to-end Learned image compression (LIC) has reached the traditional hand-crafted methods such as BPG (HEVC intra) in terms of the coding gain. However, the large network size pr…
Learned Video Compression with Residual Prediction and Loop Filter
Chao Liu, Heming Sun, Jiro Katto +2
In this paper, we propose a learned video codec with a residual prediction network (RP-Net) and a feature-aided loop filter (LF-Net). For the RP-Net, we exploit the residual of pre…
Fully Neural Network Mode Based Intra Prediction of Variable Block Size
Heming Sun, Lu Yu, Jiro Katto
Intra prediction is an essential component in the image coding. This paper gives an intra prediction framework completely based on neural network modes (NM). Each NM can be regarde…
A QP-adaptive Mechanism for CNN-based Filter in Video Coding
Chao Liu, Heming Sun, Jiro Katto +2
Convolutional neural network (CNN)-based filters have achieved great success in video coding. However, in most previous works, individual models are needed for each quantization pa…
A Convolutional Neural Network-Based Low Complexity Filter
Chao Liu, Heming Sun, Jiro Katto +2
Convolutional Neural Network (CNN)-based filters have achieved significant performance in video artifacts reduction. However, the high complexity of existing methods makes it diffi…
End-to-end Learned Image Compression with Fixed Point Weight Quantization
Heming Sun, Zhengxue Cheng, Masaru Takeuchi +1
Learned image compression (LIC) has reached the traditional hand-crafted methods such as JPEG2000 and BPG in terms of the coding gain. However, the large model size of the network…
Enhanced Intra Prediction for Video Coding by Using Multiple Neural Networks
Heming Sun, Zhengxue Cheng, Masaru Takeuchi +1
This paper enhances the intra prediction by using multiple neural network modes (NM). Each NM serves as an end-to-end mapping from the neighboring reference blocks to the current c…
Low Bitrate Image Compression with Discretized Gaussian Mixture Likelihoods
Zhengxue Cheng, Heming Sun, Jiro Katto
In this paper, we provide a detailed description on our submitted method Kattolab to Workshop and Challenge on Learned Image Compression (CLIC) 2020. Our method mainly incorporates…
Learned Lossless Image Compression with a HyperPrior and Discretized Gaussian Mixture Likelihoods
Zhengxue Cheng, Heming Sun, Masaru Takeuchi +1
Lossless image compression is an important task in the field of multimedia communication. Traditional image codecs typically support lossless mode, such as WebP, JPEG2000, FLIF. Re…
Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules
Zhengxue Cheng, Heming Sun, Masaru Takeuchi +1
Image compression is a fundamental research field and many well-known compression standards have been developed for many decades. Recently, learned compression methods exhibit a fa…
Dual Learning-based Video Coding with Inception Dense Blocks
Chao Liu, Heming Sun, Junan Chen +5
In this paper, a dual learning-based method in intra coding is introduced for PCS Grand Challenge. This method is mainly composed of two parts: intra prediction and reconstruction…
Learning Image and Video Compression through Spatial-Temporal Energy Compaction
Zhengxue Cheng, Heming Sun, Masaru Takeuchi +1
Compression has been an important research topic for many decades, to produce a significant impact on data transmission and storage. Recent advances have shown a great potential of…
Deep Residual Learning for Image Compression
Zhengxue Cheng, Heming Sun, Masaru Takeuchi +1
In this paper, we provide a detailed description on our approach designed for CVPR 2019 Workshop and Challenge on Learned Image Compression (CLIC). Our approach mainly consists of…
Perceptual Quality Study on Deep Learning based Image Compression
Zhengxue Cheng, Pinar Akyazi, Heming Sun +2
Recently deep learning based image compression has made rapid advances with promising results based on objective quality metrics. However, a rigorous subjective quality evaluation…
Performance Comparison of Convolutional AutoEncoders, Generative Adversarial Networks and Super-Resolution for Image Compression
Zhengxue Cheng, Heming Sun, Masaru Takeuchi +1
Image compression has been investigated for many decades. Recently, deep learning approaches have achieved a great success in many computer vision tasks, and are gradually used in…