Date Published: May 8, 2019
Publisher: Public Library of Science
Author(s): Ming-Te Wu, Zhaoqing Pan.
In this study, a complexity-quality analysis with transcoding architectures is proposed for reducing inverse quantization numbers. This architecture is different from conventional transcoding scheme, which neglects the relationship between previous and current quantizer step size. However, the proposed transcoding architecture depends on the modulus of the ratio of the current and previous quantization parameter. By analyzing the quantized area of the previous and current quantization parameter, we concluded the part of undoing first inverse quantization, to reduce computing complexity. From computer simulation, we verify the merits of the proposed scheme over the conventional transcoding approaches, in terms of achieving better performance based on the computing complexity and objective (e.g., the peak signal-to-noise ratio) analysis.
Transcoding is very important in multimedia application. When we would like to share good videos with friends especially, it is a very well way by internet transmission. Limited to internet bandwidth, if we want to deliver video bitstreams, the bit-rate conversion problem we will face. On the other hand, it is also a transcoding problem. Generally, transcoding can be interpreted as the operation of converting a video from one format into another format . For example, an original video is encoded in an MPEG-2 format at 5.3Mb/s, the temporal rate is 30 f/s, and the input resolution is 720×480. Then the original video is transcoded to an MPEG-4 format at 128Kb/s, the temporal rate is 10f/s, and the output resolution is 352×240 . However, the meaning of transcoding is not only an operation of format-conversion but also it can share popular video-audio to another people through the internet or satellite media. This will propagate information unlimitedly.
In this section, we proposed a new architecture which according to the modulus that the quantized step size at transcoder divides the quantized step size at the encoder. We designed several different transcoding processes according to the different modulus of quantization ratio cases. This benefits that transcoding will spend the least computing complexity and maintain the same performance. We will do the computing complexity reduction analysis by PSNR measure objectively and vision measure subjectively in Sec.3.
In this section, the superiority, in terms of good visual quality and good peak signal noise ratio (PSNR), of the proposed scheme is verified using computer simulation. For comparison, the 352×288 CIF and 3840×2160 4-k ultra-HD test sequences, viz., Foreman, Susie, Mobile & Calendar, Cactus and Flower Garden are chosen for the data compression process and adopted as simulation sequences. The experiments are performed on a Pentium-IV 1.6GHz PC. Several experiments are made in MPEG II. In fact, the proposed method can be implemented in any coding standard because all transcoding architecture need to process the I-picture of decoding/encoding. From Table 2, we can see that our proposed method is faster than CPDT about 21.3fps, SDDT about 5fps, CDDT about 14.2fps in IPPP… case for the Foreman sequences. In IBBP… case, our proposed method is faster than CPDT about 14.2fps, SDDT about 4.2fps, CDDT about 11.6fps for the Foreman sequences. Besides, we can see that our proposed method is faster than CPDT about 21.5fps, SDDT about 5.1fps, CDDT about 12.4fps in IPPP… case for the Mobile & Calendar sequences. In IBBP… case, our proposed method is faster than CPDT about 15.1fps, SDDT about 4.3fps, CDDT about 11.1fps for the Mobile & Calendar sequences. Table 3 shows our proposed method has better PSNR than CPDT approach about 0.12~0.42 dB and CPDT+FDVS  scheme about 0.05~0.28 dB. In Fig 10, the PSNR of our proposed method was about 0.1–0.3 dB less than that of the direct encoding approach but perform better than cascaded quantization transcoding for the Flower Garden sequences. However, the complexity in I-picture transcoding of the proposed scheme was reduced by about 20%, while maintaining good visual performance. Additionally, Fig 11 displays that the proposed system has better objective performance than the other methods. In addition, 4-k ultra-HD video clips are test in Table 4.
In this paper, we have proposed a new modified version of transcoding architecture, with auto-selective architecture capability, for computational complexity reduction of bitstreams, during video transcoding processes. Experimental results show that our method can obtain good vision and PSNR performance in comparison with other approaches.