No abstract

Abstract-Scalable Video Coding (SVC) / H.264 is one type of video compression techniques. Which provided more reality in dealing with video compression to provide an efficient video coding based on H.264/AVC. This ensures higher performance through high compression ratio. SVC/H.264 is a complexity technique whereas the takes considerable time for computation the best mode of macroblock and motion estimation through using the exhaustive search techniques. This work reducing the processing time through matching between the complexity of the video and the method of selection macroblock and motion estimation. The goal of this approach is reducing the encoding time and improving the quality of video stream the efficiency of the proposed approach makes it suitable for are many applications as video conference application and security application.

Scalable Video Coding (SVC) is an international standard technique for video compression. It is an extension of H.264 Advanced Video Coding (AVC). In the encoding of video streams by SVC, it is suitable to employ the macroblock (MB) mode because it affords superior coding efficiency. However, the exhaustive mode decision technique that is usually used for SVC increases the computational complexity, resulting in a longer encoding time (ET). Many other algorithms were proposed to solve this problem with imperfection of increasing transmission time (TT) across the network. To minimize the ET and TT, this paper introduces four efficient algorithms based on spatial scalability. The algorithms utilize the mode-distribution correlation between the base layer (BL) and enhancement layers (ELs) and interpolation between the EL frames. The proposed algorithms are of two categories. Those of the first category are based on interlayer residual SVC spatial scalability. They employ two methods, namely, interlayer interpolation (ILIP) and the interlayer base mode (ILBM) method, and enable ET and TT savings of up to 69.3% and 83.6%, respectively. The algorithms of the second category are based on full-search SVC spatial scalability. They utilize two methods, namely, full interpolation (FIP) and the full-base mode (FBM) method, and enable ET and TT savings of up to 55.3% and 76.6%, respectively.

Video surveillance systems are essential as other application domain. Handling efficient and reliable for underground projects as well surveillance image is so significant to ensure security and safety. The wireless channels are efficient as data transferring media. On the other hand, the bandwidth may be limited for some environmental conditions. Hence, the image compression algorithm is very important to be conducted and applied to save the transmission bandwidth. This paper presents an image compression algorithm for video surveillance. The method is based on the concept of luminance variation of image. The image compression method is expected to achieve a reasonable compression ratio with acceptable quality. With another meaning, the compressed image size is decreased and consumes a smaller transmission bandwidth via the wireless channel compared with the original image size. The method adopts a deep learning approach to improve the quality with limited bandwidth. The proposed method is abbreviated as DLBL (deep learning block luminance). DLBL implemented and tested on some tested bed images. The performance of the proposed method is compared with some ones considering the same conditions. Some measurable criteria are taken into consideration for performance evaluation. The criteria are the compression ratio (CR), peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM). From the experiments results, the proposed method showed significant and efficient performance compared with some other related ones. This is clear from the values of CR, PSNR and SSIM.

Filter bank multicarrier (FBMC) is an important system used in fifth generation (5G) networks to maximize available bandwidth while meeting high spectral efficiency requirements (SE). Multicarrier modulation (MC) is an alternative modulation method used by FBMC. It is a viable alternative to the orthogonal frequency division multiplexing (OFDM) modulation method. This paper focuses on the joint channel estimation and interference cancellation (JCEIC) in FBMC systems. Recurrent neural networks (RNN) are used to estimate the ideal channel and get back the correct transmitted signal with low BER. We estimate the channel for doubly-selective channels using scattered pilots in the time and frequency correlation, and we use low-complexity interference cancellation. For JCEIC, RNN is proposed. The JCEIC algorithms' output sequences are used as inputs for the RNN. The simulation results show that the suggested technique is close to the ideal channel and has a higher BER than the other earlier methods.