Joint Learned and Traditional Video Compression for P Frame

Wang, Zhao; Liao, Ru-Ling; Ye, Yan

doi:10.1109/cvprw50498.2020.00075

Cited by 7 publications

(8 citation statements)

References 5 publications

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“…The papers [24][25][26][27][28][29][30] analyzed the shortcomings of modern video data coding methods from the point of view of ensuring the required level of reliability. Thus, the main disadvantage of existing compression coding methods is the avalanche effect of errors occurring in data transmission channels [32][33][34][35].…”

Section: Related Workmentioning

confidence: 99%

Compression Coding Method Using Internal Restructuring of Information Space

Karlov¹,

Tupitsya²,

Parkhomenko³

et al. 2022

IJC

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The subject of the study in this article is data transmission processes of the video information resource in the information communication systems of the air segment under the conditions of errors in the data transmission channel. The purpose of the article is the development of the method of compression coding in order to ensure an increase in the level of reliability of video information resources under the conditions of errors in communication channels. The following tasks are identified: to develop a method of compression coding using structural decomposition of statistical space; analyze the effectiveness of the developed method from the standpoint of ensuring the required level of reliability. The following results are obtained: the developed method of encoding video information allows increasing the level of reliability in the conditions of the transmission of video information resources in the information communication systems of the air segment due to the localization of the action of errors.

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Section: Related Workmentioning

confidence: 99%

Compression Coding Method Using Internal Restructuring of Information Space

Karlov¹,

Tupitsya²,

Parkhomenko³

et al. 2022

IJC

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“…Note that the restoration module is only activated during the training phase. CSP/CSP‐conv refers to Cross Stage Partial Network [WLW*20] with/without residual networks, DTFE refers to Dynamic Transformer Feature Enhancement module, and SC Conv refers to self‐calibrated convolutions [LHC*20].…”

Section: Togethernetmentioning

confidence: 99%

TogetherNet: Bridging Image Restoration and Object Detection Together via Dynamic Enhancement Learning

Wang

Yan

Zhang

et al. 2022

Computer Graphics Forum

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Adverse weather conditions such as haze, rain, and snow often impair the quality of captured images, causing detection networks trained on normal images to generalize poorly in these scenarios. In this paper, we raise an intriguing question -if the combination of image restoration and object detection, can boost the performance of cutting-edge detectors in adverse weather conditions. To answer it, we propose an effective yet unified detection paradigm that bridges these two subtasks together via dynamic enhancement learning to discern objects in adverse weather conditions, called TogetherNet. Different from existing efforts that intuitively apply image dehazing/deraining as a pre-processing step, TogetherNet considers a multi-task joint learning problem. Following the joint learning scheme, clean features produced by the restoration network can be shared to learn better object detection in the detection network, thus helping TogetherNet enhance the detection capacity in adverse weather conditions. Besides the joint learning architecture, we design a new Dynamic Transformer Feature Enhancement module to improve the feature extraction and representation capabilities of TogetherNet. Extensive experiments on both synthetic and real-world datasets demonstrate that our TogetherNet outperforms the state-of-the-art detection approaches by a large margin both quantitatively and qualitatively. Source code is available at https://github.com/yz-wang/TogetherNet.

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“…coding video information resources, built on the conceptual basis of algorithms of the JPEG platform [18][19][20][21]. However, these technologies have a number of problematic factors [22][23][24]. The main ones are [23 -28]:…”

Section: Introductionmentioning

confidence: 99%

“…1. Complexity of algorithmic implementation [23,25]. This is due to the fact that the implementation of algorithms of the JPEG family involves a number of stages of processing, and therefore increases the time required for data processing.…”

Section: Introductionmentioning

confidence: 99%

Method for quantitative criterion based transformation of the video information alphabet

Khmelevskiy

Tupitsya

Хмелевська

et al. 2022

reks

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Subject of study: technologies implemented in modern video coding algorithms to ensure the appropriate level of reliability in the conditions of their compact presentation. The goal is to develop a technology for transforming the alphabet of a video information based on a quantitative criterion while ensuring the required quality in networks. Objectives: to formulate requirements to video images in dynamic video surveillance systems; to analyze the existing factors leading to an imbalance between the compression and quality characteristics of existing video coding algorithms; to develop a technology for transforming the alphabet of a video information based on a quantitative criterion (attribute) for the best presentation of the encoded data; to develop a mathematical model for the formation of a quantitativeindicator for the transformation of the video images; to analyze the effectiveness of using the developed mathematical model for the formation of a quantitative indicator to provide the required trustworthiness of data for the video information resource; to assess the effectiveness of the developed technology for transforming the original message in terms of a quantitative indicator to ensure the best presentation of the encoded data; to investigate the dynamics of the probabilistic and statistical characteristics of the original message as a result of transformation according to the quantitative criterion of the significance of the elements. The research methods: compression coding methods implemented on the basis of the JPEG algorithms. The research results: a new approach has been proposed based on the transformation of the encoded alphabet of data by use of a quantitative criterion. A mathematical model has been developed for the formation of a quantitative attributethat determines the significance of the elements of the original message. Conclusions. A technology has been developed for transforming the alphabet of the original message, which allows creating conditions for a more profitable presentation of the encoded data due to a significant increase in the dynamic range of probabilistic and statistical characteristics for the transformed message while ensuring the required level of video image quality.

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Joint Learned and Traditional Video Compression for P Frame

Cited by 7 publications

References 5 publications

Compression Coding Method Using Internal Restructuring of Information Space

Compression Coding Method Using Internal Restructuring of Information Space

TogetherNet: Bridging Image Restoration and Object Detection Together via Dynamic Enhancement Learning

Method for quantitative criterion based transformation of the video information alphabet

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