Towards Real-Time Neural Video Codec for Cross-Platform Application Using Calibration Information

• URL: http://arxiv.org/abs/2309.11276v1
• Date: Wed, 20 Sep 2023 13:01:15 GMT
• Title: Towards Real-Time Neural Video Codec for Cross-Platform Application Using Calibration Information
• Authors: Kuan Tian, Yonghang Guan, Jinxi Xiang, Jun Zhang, Xiao Han, Wei Yang
• Abstract summary: Cross-platform computational errors resulting from floating point operations can lead to inaccurate decoding of the bitstream. The high computational complexity of the encoding and decoding process poses a challenge in achieving real-time performance. A real-time cross-platform neural video is capable of efficiently decoding of 720P video bitstream from other encoding platforms on a consumer-grade GPU.
• Score: 17.141950680993617
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The state-of-the-art neural video codecs have outperformed the most sophisticated traditional codecs in terms of RD performance in certain cases. However, utilizing them for practical applications is still challenging for two major reasons. 1) Cross-platform computational errors resulting from floating point operations can lead to inaccurate decoding of the bitstream. 2) The high computational complexity of the encoding and decoding process poses a challenge in achieving real-time performance. In this paper, we propose a real-time cross-platform neural video codec, which is capable of efficiently decoding of 720P video bitstream from other encoding platforms on a consumer-grade GPU. First, to solve the problem of inconsistency of codec caused by the uncertainty of floating point calculations across platforms, we design a calibration transmitting system to guarantee the consistent quantization of entropy parameters between the encoding and decoding stages. The parameters that may have transboundary quantization between encoding and decoding are identified in the encoding stage, and their coordinates will be delivered by auxiliary transmitted bitstream. By doing so, these inconsistent parameters can be processed properly in the decoding stage. Furthermore, to reduce the bitrate of the auxiliary bitstream, we rectify the distribution of entropy parameters using a piecewise Gaussian constraint. Second, to match the computational limitations on the decoding side for real-time video codec, we design a lightweight model. A series of efficiency techniques enable our model to achieve 25 FPS decoding speed on NVIDIA RTX 2080 GPU. Experimental results demonstrate that our model can achieve real-time decoding of 720P videos while encoding on another platform. Furthermore, the real-time model brings up to a maximum of 24.2\% BD-rate improvement from the perspective of PSNR with the anchor H.265.

Related papers

• SparseTem: Boosting the Efficiency of CNN-Based Video Encoders by Exploiting Temporal Continuity [15.872209884833977]
  We propose a memory-efficient scheduling method to eliminate memory overhead and an online adjustment mechanism to minimize accuracy degradation. SparseTem achieves speedup of 1.79x for EfficientDet and 4.72x for CRNN, with minimal accuracy drop and no additional memory overhead.
  arXiv  Detail & Related papers  (2024-10-28T07:13:25Z)
• Accelerating Error Correction Code Transformers [56.75773430667148]
  We introduce a novel acceleration method for transformer-based decoders. We achieve a 90% compression ratio and reduce arithmetic operation energy consumption by at least 224 times on modern hardware.
  arXiv  Detail & Related papers  (2024-10-08T11:07:55Z)
• High-Efficiency Neural Video Compression via Hierarchical Predictive Learning [27.41398149573729]
  Enhanced Deep Hierarchical Video Compression-DHVC 2.0- introduces superior compression performance and impressive complexity efficiency. Uses hierarchical predictive coding to transform each video frame into multiscale representations. Supports transmission-friendly progressive decoding, making it particularly advantageous for networked video applications in the presence of packet loss.
  arXiv  Detail & Related papers  (2024-10-03T15:40:58Z)
• Standard compliant video coding using low complexity, switchable neural wrappers [8.149130379436759]
  We propose a new framework featuring standard compatibility, high performance, and low decoding complexity. We employ a set of jointly optimized neural pre- and post-processors, wrapping a standard video, to encode videos at different resolutions. We design a low complexity neural post-processor architecture that can handle different upsampling ratios.
  arXiv  Detail & Related papers  (2024-07-10T06:36:45Z)
• Effortless Cross-Platform Video Codec: A Codebook-Based Method [17.141950680993617]
  In certain circumstances, advanced neural video codecs can surpass the most complex traditional codecs in their rate-distortion (RD) performance. One of the main reasons for the high performance of existing neural video codecs is the use of the entropy model. In this paper, we propose a cross-platform video compression framework based on codebooks.
  arXiv  Detail & Related papers  (2023-10-16T11:28:22Z)
• Graph Neural Networks for Channel Decoding [71.15576353630667]
  We showcase competitive decoding performance for various coding schemes, such as low-density parity-check (LDPC) and BCH codes. The idea is to let a neural network (NN) learn a generalized message passing algorithm over a given graph. We benchmark our proposed decoder against state-of-the-art in conventional channel decoding as well as against recent deep learning-based results.
  arXiv  Detail & Related papers  (2022-07-29T15:29:18Z)
• Improved decoding of circuit noise and fragile boundaries of tailored surface codes [61.411482146110984]
  We introduce decoders that are both fast and accurate, and can be used with a wide class of quantum error correction codes. Our decoders, named belief-matching and belief-find, exploit all noise information and thereby unlock higher accuracy demonstrations of QEC. We find that the decoders led to a much higher threshold and lower qubit overhead in the tailored surface code with respect to the standard, square surface code.
  arXiv  Detail & Related papers  (2022-03-09T18:48:54Z)
• Conditional Entropy Coding for Efficient Video Compression [82.35389813794372]
  We propose a very simple and efficient video compression framework that only focuses on modeling the conditional entropy between frames. We first show that a simple architecture modeling the entropy between the image latent codes is as competitive as other neural video compression works and video codecs. We then propose a novel internal learning extension on top of this architecture that brings an additional 10% savings without trading off decoding speed.
  arXiv  Detail & Related papers  (2020-08-20T20:01:59Z)
• Video compression with low complexity CNN-based spatial resolution adaptation [15.431248645312309]
  spatial resolution adaptation can be integrated within video compression to improve overall coding performance. A novel framework is proposed which supports the flexible allocation of complexity between the encoder and decoder.
  arXiv  Detail & Related papers  (2020-07-29T10:20:36Z)
• Learning for Video Compression with Recurrent Auto-Encoder and Recurrent Probability Model [164.7489982837475]
  This paper proposes a Recurrent Learned Video Compression (RLVC) approach with the Recurrent Auto-Encoder (RAE) and Recurrent Probability Model ( RPM) The RAE employs recurrent cells in both the encoder and decoder to exploit the temporal correlation among video frames. Our approach achieves the state-of-the-art learned video compression performance in terms of both PSNR and MS-SSIM.
  arXiv  Detail & Related papers  (2020-06-24T08:46:33Z)
• Content Adaptive and Error Propagation Aware Deep Video Compression [110.31693187153084]
  We propose a content adaptive and error propagation aware video compression system. Our method employs a joint training strategy by considering the compression performance of multiple consecutive frames instead of a single frame. Instead of using the hand-crafted coding modes in the traditional compression systems, we design an online encoder updating scheme in our system.
  arXiv  Detail & Related papers  (2020-03-25T09:04:24Z)

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.