Xuan-Tu Tran, Ngoc-Sinh Nguyen, Duy-Hieu Bui, Minh-Trien Pham, Hung K. Nguyen, C. Pham
{"title":"Reducing Bitrate and Increasing the Quality of Inter Frame by Avoiding Quantization Errors in Stationary Blocks","authors":"Xuan-Tu Tran, Ngoc-Sinh Nguyen, Duy-Hieu Bui, Minh-Trien Pham, Hung K. Nguyen, C. Pham","doi":"10.4108/EAI.24-10-2019.162795","DOIUrl":null,"url":null,"abstract":"In image compression and video coding, quantization error helps to reduce the amount of information of the high frequency components. However, in temporal prediction the quantization error contributes its value as noise in the total residual information. Therefore, the residual signal of the inter-picture prediction is greater than the expected one and always differs zero value even input video contains only homogeneous frames. In this paper, we reveal negative effects of quantization errors in inter prediction and propose a video encoding scheme which is able to avoid side effects of quantization errors in the stationary parts. We propose to implement a motion detection algorithm as the first stage of video encoding to separate the video into two parts: motion and static. The motion information allows us to force residual data of non-changed part to zero and keep the residual signal of motion regularly. Beside, we design block-based filters which improve motion results and filter those results fit into block encode size well. Fixed residual data of static information permits us to pre-calculate its quantized coefficient and create a bypass encoding path for it. Experimental results with the JPEG compression (MJPEG-DPCM) showed that the proposed method produces lower bitrate than the conventional MJPEG-DPCM at the same quantization parameter and a lower computational complexity. Received on 13 September 2019; accepted on 11 December 2019; published on 17 January 2020","PeriodicalId":33474,"journal":{"name":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","volume":"20 1","pages":"e2"},"PeriodicalIF":0.0000,"publicationDate":"2020-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"EAI Endorsed Transactions on Industrial Networks and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4108/EAI.24-10-2019.162795","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0
Abstract
In image compression and video coding, quantization error helps to reduce the amount of information of the high frequency components. However, in temporal prediction the quantization error contributes its value as noise in the total residual information. Therefore, the residual signal of the inter-picture prediction is greater than the expected one and always differs zero value even input video contains only homogeneous frames. In this paper, we reveal negative effects of quantization errors in inter prediction and propose a video encoding scheme which is able to avoid side effects of quantization errors in the stationary parts. We propose to implement a motion detection algorithm as the first stage of video encoding to separate the video into two parts: motion and static. The motion information allows us to force residual data of non-changed part to zero and keep the residual signal of motion regularly. Beside, we design block-based filters which improve motion results and filter those results fit into block encode size well. Fixed residual data of static information permits us to pre-calculate its quantized coefficient and create a bypass encoding path for it. Experimental results with the JPEG compression (MJPEG-DPCM) showed that the proposed method produces lower bitrate than the conventional MJPEG-DPCM at the same quantization parameter and a lower computational complexity. Received on 13 September 2019; accepted on 11 December 2019; published on 17 January 2020