Feature Map Compression for Video Coding for Machines Based on Receptive Block Based Principal Component Analysis

Lee, Minhun; Choi, Hansol; Kim, Jihoon; Do, Jihoon; Kwon, Hyoungjin; Jeong, Se Yoon; Sim, Donggyu; Oh, Seoung-Jun

doi:10.1109/access.2023.3254589

Cited by 1 publication

(5 citation statements)

References 34 publications

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“…Figure 2 showcases the overall pipeline of the proposed method. Similar to previous works [11][12][13][14], our method utilizes PCA to reduce the dimensions of the multi-level feature maps intended for compression. Previous methods pose certain drawbacks: they necessitate performing an independent PCA for one or more feature maps for each input datum during the encoding process, and they compress the basis In an effort to circumvent these issues, a transform-based feature map compression method employing a predefined generalized basis matrix and mean vector is proposed.…”

Section: Proposed Transform-based Feature Map Compression Methodsmentioning

confidence: 99%

“…This allows efficient compression while preserving the accuracy of machine vision tasks. This work extends our contribution to the 140th MPEG meeting [19]. The remainder of this paper is organized as follows.…”

Section: Introductionmentioning

confidence: 91%

“…One such method is Lloyd-Max quantization [21]. However, prior studies have indicated that the impact on performance from errors in the feature map domain due to quantization is not substantial [11]. Therefore, the previous feature map compression methods and the MPEG-VCM [22] feature anchor generation process adopted a uniform integer quantization method, relying only on the minimum and maximum values and bit depth [11][12][13][14].…”

Section: Quantization and Packingmentioning

confidence: 99%

“…Specifically, P-layer feature maps (P2, P3, P4, and P5) were targeted for object detection and instance segmentation, while L-layer feature maps (L0, L1, and L2) were used for object tracking, as depicted in Figure 1. However, the total data of multi-level FPN feature maps are significantly larger than the data size of an input image [11]. Therefore, several methods have been proposed to eradicate redundancy within and between multi-level feature maps using a transform-based mechanism.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, several methods have been proposed to eradicate redundancy within and between multi-level feature maps using a transform-based mechanism. Lee et al [11] proposed a receptive block-based principal component analysis (RPCA) for feature map compression. While superior to discrete cosine transform-based compression methods, this approach is limited when compressing low-level feature maps, which constitute more than half the data among multilevel feature maps.…”

Section: Introductionmentioning

confidence: 99%

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Transform-Based Feature Map Compression Method for Video Coding for Machines (VCM)

Lee,

Park,

et al. 2023

Electronics

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The burgeoning field of machine vision has led to the development by the Moving Picture Experts Group (MPEG) of a new type of compression technology called video coding for machines (VCM), to enhance machine recognition through video information compression. This research proposes a principal component analysis (PCA)-based compression methodology for multi-level feature maps extracted from the feature pyramid network (FPN) structure. Unlike current PCA-based studies that independently carry out PCA for each feature map, our approach employs a generalized basis matrix and mean vector derived from channel correlations by a generalized PCA process to eliminate the need for a PCA process. Further compression is achieved by amalgamating high-dimensional feature maps, capitalizing on the spatial redundancy within these multi-level feature maps. As a result, the proposed VCM encoder forgoes the PCA process, and the generalized data do not incur any compression loss. It only requires compressing the coefficients for each feature map using versatile video coding (VVC). Experimental results demonstrate superior performance by our method over all feature anchors for each machine vision task, as specified by the MPEG-VCM common test conditions, outperforming previous PCA-based feature map compression methods. Notably, it achieved an 89.3% BD-rate reduction for instance segmentation tasks.

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Section: Proposed Transform-based Feature Map Compression Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 91%