When Video Coding Meets Multimodal Large Language Models: A Unified Paradigm for Video Coding

• URL: http://arxiv.org/abs/2408.08093v1
• Date: Thu, 15 Aug 2024 11:36:18 GMT
• Title: When Video Coding Meets Multimodal Large Language Models: A Unified Paradigm for Video Coding
• Authors: Pingping Zhang, Jinlong Li, Meng Wang, Nicu Sebe, Sam Kwong, Shiqi Wang,
• Abstract summary: Cross-Modality Video Coding (CMVC) is a pioneering approach to explore multimodality representation and video generative models in video coding. During decoding, previously encoded components and video generation models are leveraged to create multiple encoding-decoding modes. Experiments indicate that TT2V achieves effective semantic reconstruction, while IT2V exhibits competitive perceptual consistency.
• Score: 112.44822009714461
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing codecs are designed to eliminate intrinsic redundancies to create a compact representation for compression. However, strong external priors from Multimodal Large Language Models (MLLMs) have not been explicitly explored in video compression. Herein, we introduce a unified paradigm for Cross-Modality Video Coding (CMVC), which is a pioneering approach to explore multimodality representation and video generative models in video coding. Specifically, on the encoder side, we disentangle a video into spatial content and motion components, which are subsequently transformed into distinct modalities to achieve very compact representation by leveraging MLLMs. During decoding, previously encoded components and video generation models are leveraged to create multiple encoding-decoding modes that optimize video reconstruction quality for specific decoding requirements, including Text-Text-to-Video (TT2V) mode to ensure high-quality semantic information and Image-Text-to-Video (IT2V) mode to achieve superb perceptual consistency. In addition, we propose an efficient frame interpolation model for IT2V mode via Low-Rank Adaption (LoRA) tuning to guarantee perceptual quality, which allows the generated motion cues to behave smoothly. Experiments on benchmarks indicate that TT2V achieves effective semantic reconstruction, while IT2V exhibits competitive perceptual consistency. These results highlight potential directions for future research in video coding.

Related papers

• Generative Human Video Compression with Multi-granularity Temporal Trajectory Factorization [13.341123726068652]
  We propose a novel Multi-granularity Temporal Trajectory Factorization framework for generative human video compression. Experimental results show that proposed method outperforms latest generative models and the state-of-the-art video coding standard Versatile Video Coding.
  arXiv  Detail & Related papers  (2024-10-14T05:34:32Z)
• xGen-VideoSyn-1: High-fidelity Text-to-Video Synthesis with Compressed Representations [120.52120919834988]
  xGen-SynVideo-1 is a text-to-video (T2V) generation model capable of producing realistic scenes from textual descriptions. VidVAE compresses video data both spatially and temporally, significantly reducing the length of visual tokens. DiT model incorporates spatial and temporal self-attention layers, enabling robust generalization across different timeframes and aspect ratios.
  arXiv  Detail & Related papers  (2024-08-22T17:55:22Z)
• CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer [55.515836117658985]
  We present CogVideoX, a large-scale text-to-video generation model based on diffusion transformer. It can generate 10-second continuous videos aligned with text prompt, with a frame rate of 16 fps and resolution of 768 * 1360 pixels.
  arXiv  Detail & Related papers  (2024-08-12T11:47:11Z)
• VideoGPT+: Integrating Image and Video Encoders for Enhanced Video Understanding [15.959757105308238]
  Video LMMs rely on either image or video encoders to process visual inputs, each of which has its own limitations. We introduce VideoGPT+, which combines the complementary benefits of the image encoder (for detailed spatial understanding) and the video encoder (for global temporal context modeling) Our architecture showcases improved performance across multiple video benchmarks, including VCGBench, MVBench and Zero-shot question-answering.
  arXiv  Detail & Related papers  (2024-06-13T17:59:59Z)
• Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization [52.63845811751936]
  Video pre-training is challenging due to the modeling of its dynamics video. In this paper, we address such limitations in video pre-training with an efficient video decomposition. Our framework is both capable of comprehending and generating image and video content, as demonstrated by its performance across 13 multimodal benchmarks.
  arXiv  Detail & Related papers  (2024-02-05T16:30:49Z)
• Implicit-explicit Integrated Representations for Multi-view Video Compression [40.86402535896703]
  We propose an implicit-explicit integrated representation for multi-view video compression. The proposed framework combines the strengths of both implicit neural representation and explicit 2D datasets. Our proposed framework can achieve comparable or even superior performance to the latest multi-view video compression standard MIV.
  arXiv  Detail & Related papers  (2023-11-29T04:15:57Z)
• Control-A-Video: Controllable Text-to-Video Diffusion Models with Motion Prior and Reward Feedback Learning [50.60891619269651]
  Control-A-Video is a controllable T2V diffusion model that can generate videos conditioned on text prompts and reference control maps like edge and depth maps. We propose novel strategies to incorporate content prior and motion prior into the diffusion-based generation process. Our framework generates higher-quality, more consistent videos compared to existing state-of-the-art methods in controllable text-to-video generation.
  arXiv  Detail & Related papers  (2023-05-23T09:03:19Z)
• Deep Video Coding with Dual-Path Generative Adversarial Network [39.19042551896408]
  This paper proposes an efficient codecs namely dual-path generative adversarial network-based video (DGVC) Our DGVC reduces the average bit-per-pixel (bpp) by 39.39%/54.92% at the same PSNR/MS-SSIM.
  arXiv  Detail & Related papers  (2021-11-29T11:39:28Z)
• An Emerging Coding Paradigm VCM: A Scalable Coding Approach Beyond Feature and Signal [99.49099501559652]
  Video Coding for Machine (VCM) aims to bridge the gap between visual feature compression and classical video coding. We employ a conditional deep generation network to reconstruct video frames with the guidance of learned motion pattern. By learning to extract sparse motion pattern via a predictive model, the network elegantly leverages the feature representation to generate the appearance of to-be-coded frames.
  arXiv  Detail & Related papers  (2020-01-09T14:18:18Z)

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.