Efficient feature coding based on performance analysis of Versatile Video Coding (VVC) in Video Coding for Machines (VCM)

Lee, Jin Young; Choi, Yongho; Le, The Van; Choi, Kiho

doi:10.1007/s11042-023-15409-7

Cited by 2 publications

(2 citation statements)

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“…Fischer et al [15] explored the influence of three VVC in-loop filters-the deblocking filter, sample adaptive offset filter, and adaptive loop filter-by toggling these options on and off for object detection tasks. Similarly, Lee et al [16] examined the influence of VVC-based feature coding for machines and its applicability to video captioning, proposing a combination of options for enhanced performance. However, these studies only investigate the effects of encoding options on the specific dataset, reporting the outcomes accordingly.…”

Section: Traditional Codec-based Vcmmentioning

confidence: 99%

“…MPEG experts and academic researchers have made significant strides in exploring methods to enhance video compression performance for machine vision tasks, primarily through preprocessing steps that consider ROIs identified by machine vision, and temporal and spatial resampling [13,14,16,19,22,29,30]. While much of this work has adopted the VVC test model (VTM) as the encoder that uses standard VTM encoding options, it is essential to note that VVC is optimized for human vision tasks, which means its default settings may not be consistently ideal for machine vision applications.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Guided Video Compression for Machine Vision Tasks

Kim,

Woo,

Park

et al. 2024

Preprint

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In the video compression industry, video compression tailored to machine vision tasks has recently emerged as a critical area of focus. Given the unique characteristics of machine vision, the current practice of directly employing conventional codecs reveals inefficiency, which requires compressing unnecessary regions. In this paper, we propose a framework that more aptly encodes video regions distinguished by machine vision to enhance coding efficiency. For that, the proposed framework consists of deep learning-based adaptive switch networks that guide the efficient coding tool for video encoding. Through the experiments, it is demonstrated that the proposed framework has superiority over the latest standardization project, video coding for machine benchmark, which achieves a Bjontegaard delta (BD)-rate gain of 5.91% on average and reaches up to a 19.51% BD-rate gain.

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Section: Traditional Codec-based Vcmmentioning

confidence: 99%