Abstract
Measurement coding compresses the output of compressive image sensors to improve the image/video transmission efficiency. In these coding systems, rate control plays a vital role. The major purpose of rate control is to determine the quantization parameters (or quantization stepsizes) to control the bitrate under available bandwidth (bits limitation) while maximizing the image/video quality. However, most of the existing rate control algorithms apply iterations to find the best quantization parameters, so it suffers from a long processing time and can’t efficiently support video processing. This paper presents a frame adaptive rate control scheme for measurement coding. Firstly, the initialized quantization parameter (QP) of the first frame is determined by the triangle quantization method. Moreover, frame adaptive QP adjustment is proposed to refine the QP for each frame. As a result, this work improves the video quality up to 1.56 dB PSNR and reduces the processing time up to 53% compared to the state-of-the-art.
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Kimishima, F., Yang, J., Tran, T.T.T., Zhou, J. (2022). Frame Adaptive Rate Control Scheme for Video Compressive Sensing. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13231. Springer, Cham. https://doi.org/10.1007/978-3-031-06427-2_21
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DOI: https://doi.org/10.1007/978-3-031-06427-2_21
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