# Object Detection-Based Video Compression
###### Abstract  
Video compression is designed to provide good subjective image quality, even at a high-compression ratio. In addition, video quality metrics have been used to show the results can maintain a high Peak Signal-to-Noise Ratio (PSNR), even at high compression. However, there are many difficulties in object recognition on the decoder side due to the low image quality caused by high compression. Accordingly, providing good image quality for the detected objects is necessary for the given total bitrate for utilizing object detection in a video decoder. In this paper, object detection-based video compression by the encoder and decoder is proposed that allocates lower quantization parameters to the detected-object regions and higher quantization parameters to the background. Therefore, better image quality is obtained for the detected objects on the decoder side. Object detection-based video compression consists of two types: Versatile Video Coding (VVC) and object detection. In this paper, the decoder performs the decompression process by receiving the bitstreams in the object-detection decoder and the VVC decoder. In the proposed method, the VVC encoder and decoder are processed based on the information obtained from object detection. In a random access (RA) configuration, the average Bjontegaard Delta (BD)-rates of Y, Cb, and Cr increased by 2.33%, 2.67%, and 2.78%, respectively. In an All Intra (AI) configuration, the average BD-rates of Y, Cb, and Cr increased by 0.59%, 1.66%, and 1.42%, respectively. In an RA configuration, the averages of \(\Delta\)Y-PSNR, \(\Delta\)Cb-PSNR, and \(\Delta\)Cr-PSNR for the object-detected areas improved to 0.17%, 0.23%, and 0.04%, respectively. In an AI configuration, the averages of \(\Delta\)Y-PSNR, \(\Delta\)Cb-PSNR, and \(\Delta\)Cr-PSNR for the object-detected areas improved to 0.71%, 0.30%, and 0.30%, respectively. Subjective image quality was also improved in the object-detected areas. object detection; video compression; VVC (Versatile Video Coding); video coding application; quantization +
Footnote †: journal: Applied  
divided into a sequence of CTUs. The CTU concept is same as that of HEVC [3; 4]. For a picture that has three sample arrays, a CTU consists of an N \(\times\) N (height \(\times\) width) block of luma samples together with two corresponding blocks of chroma samples. The maximum allowed size of the luma block in the CTU is specified to be 128 \(\times\) 128 [7]. The CTU can be recursively partitioned to 4 \(\times\) 4 CUs in terms of rate-distortion (RD) cost. This process is called rate-distortion optimization (RDO). The RDO process is the measurement of finding the lowest RD cost for every CU, consisting of the CTU in which the CUs having the lowest RD cost in the CTU are the best coding units. While the CUs are recursively partitioned, the main coding tools of VVC, such as intra prediction [8], inter prediction [9], transform, quantization, inverse transform, and dequantization [10], are used in the compression. After all the CTUs are coded in the picture, some of the tools, including the deblocking filter [11], Sample Adaptive Offset (SAO) [12], and Adaptive Loop Filter (ALF) [13], enhance the subjective quality of video sequences. Finally, Context Adaptive Binary Arithmetic Coding (CABAC) is used to generate a bitstream in the encoder to send to the decoder. Some of tools in VVC show dominant high coding efficiency compared with the previous video coding standards, HEVC and AVC. The dominant coding tools are listed as follows: diversification of block partitioning, Cross-Component Linear Model (CCLM), affine motion model, Adaptive Motion Vector Resolution (AMVR), Multiple Transform Selection (MTS), dependent quantization, and ALF. Through these coding tools, VVC has about 40% greater coding efficiency than HEVC for UHD sequences in terms of BD-rate, which is the objective quality measure [14; 15]. The new-generation video coding, with its high compression rate, needs to embrace broad areas of video contents.