Adversarial Video Compression Guided by Soft Edge Detection

Kim, Sung‐Soo; Park, Jin-Soo; Bampis, Christos G.; Lee, Jae‐Seong; Markey, Mia K.; Dimakis, Alexandros G.; Bovik, Alan C.

doi:10.1109/icassp40776.2020.9054165

Cited by 6 publications

(1 citation statement)

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“…In [51], the body pose and face mesh are detected at the encoder side and reconstructed into animated puppets at the decoder side to support video reconstruction. In [37], a generative decoder is adopted to map key frames as well as soft edges of non-key frames into the whole reconstructed frames. In [11], the edges are extracted to form the feature stream, which facilitates the image reconstruction at the decoder side.…”

Section: Feature Assisted Codingmentioning

confidence: 99%

Video Coding for Machine: Compact Visual Representation Compression for Intelligent Collaborative Analytics

Yang¹,

Huang²,

Hu³

et al. 2021

Preprint

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As an emerging research practice leveraging recent advanced AI techniques, e.g. deep models based prediction and generation, Video Coding for Machines (VCM) is committed to bridging to an extent separate research tracks of video/image compression and feature compression, and attempts to optimize compactness and efficiency jointly from a unified perspective of high accuracy machine vision and full fidelity human vision. With the rapid advances of deep feature representation and visual data compression in mind, in this paper, we summarize VCM methodology and philosophy based on existing academia and industrial efforts. The development of VCM follows a general rate-distortion optimization, and the categorization of key modules or techniques is established including features assisted coding, scalable coding, intermediate feature compression/optimization, and machine vision targeted codec, from broader perspectives of vision tasks, analytics resources, etc. From previous works, it is demonstrated that, although existing works attempt to reveal the nature of scalable representation in bits when dealing with machine and human vision tasks, there remains a rare study in the generality of low bit rate representation, and accordingly how to support a variety of visual analytic tasks. Therefore, we investigate a novel visual information compression for the analytics taxonomy problem to strengthen the capability of compact visual representations extracted from multiple tasks for visual analytics. A new perspective of task relationships versus compression is revisited. By keeping in mind the transferability among different machine vision tasks (e.g. high-level semantic and mid-level geometry-related), we aim to support multiple tasks jointly at low bit rates. In particular, to narrow the dimensionality gap between neural network generated features extracted from pixels and a variety of machine vision features/labels (e.g. scene class, segmentation labels), a codebook hyperprior is designed to compress the neural network-generated features. As demonstrated in our experiments, this new hyperprior model is expected to improve feature compression efficiency by estimating the signal entropy more accurately, which enables further investigation of the granularity of abstracting compact features among different tasks.

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Section: Feature Assisted Codingmentioning

confidence: 99%