44 citations · 203 across the 51 of their papers we have counts for
29 papers · 1 filter
Streamable Neural Video Compression: A Mixed Precision Approach for Cross-Platform Deployment
Kasidis Arunruangsirilert, Heming Sun, Jiro Katto
Neural Video Codecs (NVCs) offer unprecedented rate-distortion performance, making them highly attractive for bandwidth-constrained environments like 5G cellular networks and emerg…
Wavefront Parallelization for Efficient Learned Image Compression
Shimon Murai, Fangzheng Lin, Kasidis Arunruangsirilert +1
Autoregressive context models are foundational for learned image compression,but they suffer from slow serial inference. Existing acceleration methods such as checkerboard context…
Performance Analysis of Hardware-Accelerated 10-Bit 4:2:2 Encoding with Split-Frame Encoding for High-Fidelity V-PCC Streaming
Kasidis Arunruangsirilert, Jiro Katto
Video-based Point Cloud Compression (V-PCC) encodes volumetric data by projecting 3D geometry and texture onto 2D video frames. To prevent spatial distortion and color bleeding dur…
Sustainable Real-Time 8K60 HEVC Encoding for V2X: Repurposing Legacy NVENC Hardware at the Vehicular Edge
Kasidis Arunruangsirilert, Jiro Katto
The rapid advancement of Vehicle-to-Everything (V2X) communications and Tele-Operated Driving (ToD) demands ultra-low-latency, 8K60 video telemetry. However, deploying modern hardw…
Evolution of NVENC Efficiency: A Longitudinal Analysis of HQ and UHQ Tuning Efficiency, Latency and Energy Trade-offs
Kasidis Arunruangsirilert, Jiro Katto
The rapid expansion of uplink-intensive applications necessitates video coding solutions that balance high Rate-Distortion (RD) efficiency with ultra-low latency. This paper presen…
Evaluation of Hardware-based Video Encoders on Modern GPUs for UHD Live-Streaming
Kasidis Arunruangsirilert, Jiro Katto
Many GPUs have incorporated hardware-accelerated video encoders, which allow video encoding tasks to be offloaded from the main CPU and provide higher power efficiency. Over the ye…
FlashGMM: Fast Gaussian Mixture Entropy Model for Learned Image Compression
Shimon Murai, Fangzheng Lin, Jiro Katto
High-performance learned image compression codecs require flexible probability models to fit latent representations. Gaussian Mixture Models (GMMs) were proposed to satisfy this de…
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…
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…