Versatile Video Coding (VVC)

Saldanha, Mário; Sanchez, Gustavo; Marcon, César; Agostini, Luciano

doi:10.1007/978-3-031-11640-7_2

Cited by 2 publications

(1 citation statement)

References 31 publications

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“…Compared to the High-Efficiency Video Coding (HEVC) standard [2], VVC provides the same visual quality with 50% bit rate savings. However, this reduction in bit rate comes at the cost of a significant increase in computational complexity: ×10 more in the encoder and ×2 more in the decoder, with respect to HEVC [3]. This increase in complexity is a crucial limiting factor for both consumer computers and resourceconstrained embedded systems.…”

Section: Introductionmentioning

confidence: 99%

GPU-based parallelisation of a versatile video coding adaptive loop filter in resource-constrained heterogeneous embedded platform

Saha

Roma

Chavarrías

et al. 2023

J Real-Time Image Proc

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This paper presents a GPU-based parallelisation of an optimised versatile video decoder (VVC) adaptive loop filter (ALF) filter on a resource-constrained heterogeneous platform. The GPU has been comprehensively utilised to maximise the degree of parallelism, making the programme capable of exploiting the GPU capabilities. The proposed approach enables to accelerate the ALF computation by an average of two times when compared to an already fully optimised version of the software decoder implementation over an embedded platform. Finally, this work presents an analysis of energy consumption, showing that the proposed methodology has a negligible impact on this key parameter.

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Section: Introductionmentioning

confidence: 99%

GPU-based parallelisation of a versatile video coding adaptive loop filter in resource-constrained heterogeneous embedded platform

Saha

Roma

Chavarrías

et al. 2023

J Real-Time Image Proc

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Efficient Feature Compression for the Object Tracking Task

Henzel

Misra

2022

2022 IEEE International Conference on Image Processing (ICIP)

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In object tracking systems, often clients capture video, encode it and transmit it to a server that performs the actual machine task. In this paper we propose an alternative architecture, where we instead transmit features to the server. Specifically, we partition the Joint Detection and Embedding (JDE) person tracking network into client and server side sub-networks and code the intermediate tensors i.e. features. The features are compressed for transmission using a Deep Neural Network (DNN) we design and train specifically for carrying out the tracking task. The DNN uses trainable non-uniform quantizers, conditional probability estimators, hierarchical coding; concepts that have been used in the past for neural networks based image and video compression. Additionally, the DNN includes a novel parameterized dual-path layer that comprises of an autoencoder in one path and a convolution layer in the other. The tensor output by each path is added before being consumed by subsequent layers. The parameter value for this dual-path layer controls the output channel count and correspondingly the bitrate of transmitted bitstream. We demonstrate that our model improves coding efficiency by 43.67% over state-of-the-art Versatile Video Coding standard that codes the source video in pixel domain.

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Versatile Video Coding (VVC)

Cited by 2 publications

References 31 publications

GPU-based parallelisation of a versatile video coding adaptive loop filter in resource-constrained heterogeneous embedded platform

GPU-based parallelisation of a versatile video coding adaptive loop filter in resource-constrained heterogeneous embedded platform

Efficient Feature Compression for the Object Tracking Task

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