Naveen Srinivasamurthy

Sub-picture based rate control algorithm for achieving real time encoding and improved video quality for H.264 HD encoder on embedded video SOCs

In this paper we present a rate control algorithm to control the encoded picture size in a video sequence within a specified maximum limit. The need to control the encoded picture size arises in several video applications; (i) video conferencing to ensure the glass to glass delay is within acceptable conversational delay, (ii) video encoders complying with H.241 MTU packetization constraints to ensure that for all pictures real-time encoding can be achieved. The proposed rate control algorithm adjusts the quantization scale at the end of every row in the video sequence. Bits consumed by the already encoded macroblocks are monitored and the quantization scale is increased if the bits consumed are more than the average bits per macroblock as determined by the maximum encoded picture constraint. It is shown that in a video encoder complying with H.241 MTU packetization constraints the picture encoding time does not always meet the real-time encoding constraint. However, by using the proposed rate control algorithm in the video encoder (complying with H.241 MTU packetization constraints) it is shown that the encoding time for all pictures always meets the real-time constraint. Additionally, it is also shown that using the proposed rate control enables improvement in overall video quality. For sequence #3 the PSNR was increased by 3.8 dB and the DMOS was decreased by 44 points when the proposed rate control algorithm was used compared to the previous rate control algorithm indicating significant video quality improvements1. Thus with our proposed algorithm the dual requirements of (i) real-time encoding and (ii) improved video quality, are both simultaneously achieved.

Novel rate distortion optimized region of interest video coding for embedded video SOCs

In this paper we present a state of the art, practical, realtime, region of interest (ROI) video encoder implemented on the Texas Instruments TMS320DM3x SOC. The proposed algorithm is a novel rate distortion optimized ROI coding algorithm with low complexity making it ideal for implementing on embedded video SOCs with low computational and memory resources while achieving excellent perceptual quality. The proposed solution is a complete solution incorporating ROI processing in the entire video chain from front-end face detection to back-end video compression. It is probably one of the first video capture and compression system implemented on an embedded SOC which relies on specialized rate distortion method for ROI coding using object detection methods from the front end or user inputs. Extensive subjective evaluation has been performed on the proposed algorithm for various resolutions ranging from CIF to 1080p video resolutions at different bitrates for over 300 test cases. Significant subjective quality enhancements have been observed for video sequences over all the different video resolutions at various different bitrates. With the proposed algorithm competitive subjective quality is achieved for video conferencing sequences at 300 kbps for 720p and at 96 kbps for CIF when compared to the case where no ROI based rate distortion methods for coding are used. On the Texas Instruments TMS320DM3x SOC the ROI videoencoder achieved realtime performance for 1080p video resolution at 30 fps.