Secure AI-Driven Super-Resolution for Real-Time Mixed Reality Applications

• URL: http://arxiv.org/abs/2512.15823v1
• Date: Wed, 17 Dec 2025 16:19:18 GMT
• Title: Secure AI-Driven Super-Resolution for Real-Time Mixed Reality Applications
• Authors: Mohammad Waquas Usmani, Sankalpa Timilsina, Michael Zink, Susmit Shannigrahi,
• Abstract summary: Immersive formats such as 360 and 6DoF point cloud videos require high bandwidth and low latency.<n>This work focuses on reducing bandwidth consumption and encryption/decryption delay, two key contributors to overall latency.
• Score: 1.4741348040504454
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Immersive formats such as 360° and 6DoF point cloud videos require high bandwidth and low latency, posing challenges for real-time AR/VR streaming. This work focuses on reducing bandwidth consumption and encryption/decryption delay, two key contributors to overall latency. We design a system that downsamples point cloud content at the origin server and applies partial encryption. At the client, the content is decrypted and upscaled using an ML-based super-resolution model. Our evaluation demonstrates a nearly linear reduction in bandwidth/latency, and encryption/decryption overhead with lower downsampling resolutions, while the super-resolution model effectively reconstructs the original full-resolution point clouds with minimal error and modest inference time.

Related papers

• DiffVC-RT: Towards Practical Real-Time Diffusion-based Perceptual Neural Video Compression [38.495966630021556]
  We present DiffVC-RT, the first framework designed to achieve real-time diffusion-based Neural Video Compression (NVC)<n>We show that DiffVC-RT achieves 80.1% perceptual savings in terms of LPIPS over VTM-17.0 on HEVC dataset with real-time encoding and decoding speeds of 206 / 30 fps for 720p videos on an NVIDIA H800 GPU.
  arXiv  Detail & Related papers  (2026-01-28T12:59:25Z)
• HiStream: Efficient High-Resolution Video Generation via Redundancy-Eliminated Streaming [58.55148690302855]
  HiStream is an efficient autoregressive framework that systematically reduces redundancy across three axes.<n>On 1080p benchmarks, our primary HiStream model (i+ii) achieves state-of-the-art visual quality while demonstrating up to 76.2x faster denoising compared to the Wan2.1 baseline.<n>Our faster variant, HiStream+, applies all three optimizations, achieving a 107.5x acceleration over the baseline.
  arXiv  Detail & Related papers  (2025-12-24T18:59:58Z)
• 4DGCPro: Efficient Hierarchical 4D Gaussian Compression for Progressive Volumetric Video Streaming [52.76837132019501]
  We introduce 4DGCPro, a novel hierarchical 4D compression framework.<n>4DGCPro facilitates real-time mobile decoding and high-quality rendering via progressive volumetric video streaming.<n>We present an end-to-end entropy-optimized training scheme.
  arXiv  Detail & Related papers  (2025-09-22T08:38:17Z)
• FCA2: Frame Compression-Aware Autoencoder for Modular and Fast Compressed Video Super-Resolution [68.77813885751308]
  State-of-the-art (SOTA) compressed video super-resolution (CVSR) models face persistent challenges, including prolonged inference time, complex training pipelines, and reliance on auxiliary information.<n>We propose an efficient and scalable solution inspired by the structural and statistical similarities between hyperspectral images (HSI) and video data.<n>Our approach introduces a compression-driven dimensionality reduction strategy that reduces computational complexity, accelerates inference, and enhances the extraction of temporal information across frames.
  arXiv  Detail & Related papers  (2025-06-13T07:59:52Z)
• Plug-and-Play Versatile Compressed Video Enhancement [57.62582951699999]
  Video compression effectively reduces the size of files, making it possible for real-time cloud computing.<n>However, it comes at the cost of visual quality, challenges the robustness of downstream vision models.<n>We present a versatile-aware enhancement framework that adaptively enhance videos under different compression settings.
  arXiv  Detail & Related papers  (2025-04-21T18:39:31Z)
• H3AE: High Compression, High Speed, and High Quality AutoEncoder for Video Diffusion Models [97.45170082949552]
  Autoencoder (AE) is the key to the success of latent diffusion models for image and video generation.<n>H3AE achieves ultra-high compression ratios and real-time decoding speed on GPU and mobile.
  arXiv  Detail & Related papers  (2025-04-14T17:59:06Z)
• Towards Practical Real-Time Neural Video Compression [60.390180067626396]
  We introduce a practical real-time neural video (NVC) designed to deliver high compression ratio, low latency and broad versatility.<n>Experiments show our proposed DCVC-RT achieves an impressive average encoding/desampling speed 125.2/112.8 (frames per second) for 1080p video, while saving an average of 21% in fps compared to H.266/VTM.
  arXiv  Detail & Related papers  (2025-02-28T06:32:23Z)
• Compressed Domain Prior-Guided Video Super-Resolution for Cloud Gaming Content [39.55748992685093]
  We propose a novel lightweight network called Coding Prior-Guided Super-Resolution (CPGSR) to address the SR challenges in compressed game video content.<n>Inspired by the quantization in video coding, we propose a partitioned focal frequency loss to effectively guide the model's focus on preserving high-frequency information.
  arXiv  Detail & Related papers  (2025-01-03T12:01:36Z)
• Bits-to-Photon: End-to-End Learned Scalable Point Cloud Compression for Direct Rendering [10.662358423042274]
  We develop a point cloud compression scheme that generates a bit stream that can be directly decoded to renderable 3D Gaussians. The proposed scheme generates a scalable bit stream, allowing multiple levels of details at different bit-rate ranges. Our method supports real-time color decoding and rendering of high quality point clouds, thus paving the way for interactive 3D streaming applications with free view points.
  arXiv  Detail & Related papers  (2024-06-09T20:58:32Z)
• DiffPMAE: Diffusion Masked Autoencoders for Point Cloud Reconstruction [4.535034562610469]
  We propose an effective point cloud reconstruction architecture, inspired by self-supervised learning concepts, called DiffPMAE. By the nature of this reconstruction process, DiffPMAE can be extended to many related downstream tasks including point cloud compression, upsampling and completion. We validate the performance of DiffPMAE exceeding many state-of-the-art methods in-terms of auto-encoding and downstream tasks considered.
  arXiv  Detail & Related papers  (2023-12-06T05:39:00Z)
• CaDM: Codec-aware Diffusion Modeling for Neural-enhanced Video Streaming [15.115975994657514]
  We present Codec-aware Diffusion Modeling (CaDM), a novel Neural-enhanced Video Streaming (NVS) paradigm. First, CaDM improves the encoder's compression efficiency by simultaneously reducing resolution and color bit-depth video frames.
  arXiv  Detail & Related papers  (2022-11-15T05:14:48Z)
• COMISR: Compression-Informed Video Super-Resolution [76.94152284740858]
  Most videos on the web or mobile devices are compressed, and the compression can be severe when the bandwidth is limited. We propose a new compression-informed video super-resolution model to restore high-resolution content without introducing artifacts caused by compression.
  arXiv  Detail & Related papers  (2021-05-04T01:24:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.