Bit Rate Transcoding for High Efficiency Video Coding

igh efficiency video coding (HEVC) has demonstrated a notable increase in compression performance and is taken as a successor to H.264/AVC. Efficient bit rate adaptation algorithms are required to contain the HEVC standard between real life community facilities. A present issue of bit rate transcoding is its high computational complexity which is related with the encoder of a cascaded pixel domain transcoder. This paper gives Top to Bottom (T2B) approach to reduce complexity by using different complexity schemes. Proposed approach is effective in reducing complexity in Coding Unit (CU) optimization level. Coding Unit has been analyzed in T2B Approach. While examining the coding unit information of the input video is turned to account for decreasing the number of evaluation and early terminate the process. For the Prediction Unit (PU) level the units are powerfully chosen contingent upon likelihood of Prediction Unit sizes and co-found input prediction partitioning. By utilizing this approach, complexity scalable bit rate transcoding has achieved. Machine learning approach can be used to control computational complexity. Additionally, the T2B strategy is able to gain a spread on trade-offs in transrating complexity and coding performance. Using T2B approach 15% encoding time saving is accomplished. From this scheme, for the less resolution video 27% time saving has achieved