This paper presents the results of an informal subjective quality comparison between the current state of the emerging
High Efficiency Video Coding (HEVC) draft standard and the well-established H.264 / MPEG-4 AVC High Profile (HP)
for low-delay applications. The tests consisted of two basic encoding comparisons. First, we compare the Main profile
low-delay configuration of the HEVC reference software (HM) against a similarly configured H.264 / MPEG-4 AVC HP
reference encoder (JM). Additionally, to complement these results, the widely-recognized production-quality H.264 /
MPEG-4 AVC encoder known as x264 is compared with a production-quality HEVC implementation from eBrisk Video.
The encoding configurations are designed to reflect relevant application scenarios and to enable a fair comparison to the maximum extent feasible. When viewing HM and JM encoded video side-by-side in which the JM was configured to use approximately twice the bit rate of the HM encoded video, viewers indicated that they preferred the HM encoded video in approximately 74% of trials. Similarly, when comparing the eBrisk HEVC and x264 H.264 / MPEG-4 AVC
production encoders in which x264 was configured to use approximately twice the bit rate of the eBrisk encoded video, viewers indicated they preferred the eBrisk HEVC encoded video in approximately 62% of trials. The selection of which encoding was displayed on which side for the side-by-side viewing was established in a randomized manner, and the subjective viewing experiments were administered in a double-blind fashion. The results reported in this paper generally confirm that the HEVC design (as represented by HM version 7.1 and separately by a production-quality HEVC implementation) exhibits a substantial improvement in compression capability beyond that of H.264 / MPEG-4 AVC (as represented by a similarly-configured JM version 18.3 and x264 version core 122 r2184, respectively) for low-delay video applications, with HEVC exhibiting roughly twice or more of the overall compression capability of H.264 / MPEG-4 AVC.
The visual saliency map represents the most attractive regions in video. Automatic saliency map determination
is important in mobile video applications such as autofocusing in video capturing. It is well known that motion
plays a critical role in visual attention modeling. Motion in video consists of camera's motion and foreground
target's motion. In determining the visual saliency map, we are concerned with the foreground target's motion.
To achieve this, we evaluate the camera/global motion and then identify the moving target from the background.
Specifically, we propose a three-step procedure for visual saliency map computation: 1) motion vector (MV) field
filtering, 2) background extraction and 3) contrast map computation. In the first step, the mean value of the MV
field is treated as the camera's motion. As a result, the MV of the background can be detected and eliminated,
and the saliency map can be roughly determined. In the second step, we further remove noisy image blocks in the
background and provide a refined description of the saliency map. In the third step, a contrast map is computed
and integrated with the result of foreground extraction. All computations required in the our proposed algorithm
are low so that they can be used in mobile devices. The accuracy and robustness of the proposed algorithm is
supported by experimental results.
Block artifact deteriorates the video subjective visual quality, so it is needed to take measures to reduce the effect of block artifact. Unlike MPEG series coding system, H.264 does not use post-processing filter but adds loop-filter in motion vector compensation loop. The most essential points in loopfilter are strength- a value which indicates the strength of block artifact- and filtering operators. But loopfilter in H.264 is not so satisfactory, especially in low bitrate. A new loopfilter algorithm is proposed to improve the performance of filtering. The value of strength is evaluated by coding modes (intra and inter), estimated motion vector, flatness of image region and QP. There are 4 different filtering operators in total. Every operator which is a one-dimension filtering window corresponds a non-zero strength. The experiments show that subjective and objective quality of video can be improved remarkably.