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Adaptive live video streaming applications utilize a predefined collection of bitrate-resolution pairs, known as a bitrate ladder, for simplicity and efficiency, eliminating the need for additional run-time to determine the optimal pairs for each video. These applications do not incorporate two-pass encoding methods due to increased latency. However, an optimized bitrate ladder could result in lower storage and delivery costs and improved Quality of Experience (QoE). This paper presents a Just Noticeable Difference (JND)-aware constrained Variable Bitrate (cVBR) Two-pass Per-title encoding Scheme (JTPS) designed specifically for live video streaming. JTPS predicts a content- and JND-aware bitrate ladder using low-complexity features based on Discrete Cosine Transform (DCT) energy and optimizes the constant rate factor (CRF) for each representation. The effectiveness of JTPS is demonstrated using the x265 open-source video encoder, with an average bitrate reduction of 18.80% and 32.59% for the same PSNR and VMAF, respectively, compared to the standard HTTP Live Streaming (HLS) bitrate ladder using Constant Bitrate (CBR) encoding. The implementation of JTPS also resulted in a 68.96% reduction in storage space with a negligible impact on streaming latency.

Since video accounts for the majority of today's internet traffic, the popularity of HTTP Adaptive Streaming (HAS) is increasing steadily. In HAS, each video is encoded at multiple bitrates and spatial resolutions (i.e., representations) to adapt to a heterogeneity of network conditions, device characteristics, and end-user preferences. Most of the streaming services utilize cloud-based encoding techniques which enable a fully parallel encoding process to speed up the encoding and consequently to reduce the overall time complexity. State-of-the-art approaches further improve the encoding process by utilizing encoder analysis information from already encoded representation(s) to improve the encoding time complexity of the remaining representations. In this paper, we investigate various multi-encoding algorithms (i.e., multi-rate and multi-resolution) and propose novel multi-encoding algorithms for large-scale HTTP Adaptive Streaming deployments. Experimental results demonstrate that the proposed multi-encoding algorithm optimized for the highest compression efficiency reduces the overall encoding time by 39% with a 1.5% bitrate increase compared to stand-alone encodings. Its optimized version for the highest time savings reduces the overall encoding time by 50% with a 2.6% bitrate increase compared to standalone encodings.

Picture compression could be a wide dynamic investigation space. In the present time, a few redesigns are made in compression systems and more advancements are imperative since the excitement for limit and transmission of visual data is perseveringly on an augmentation. This paper analyses compression utilizing visual redundancy and examines a couple of customary schemes used in picture compression, couple of present day compression frameworks towards visual quality, execution, method predictability and intricacy as opposed to pixel-wise dependability. The paper demonstrates the correlation of transforms: Discrete Cosine Transform, Haar Wavelet Transform and Karhunen-Loeve Transform and picture compression schemes: JPEG and BPG for execution in an exceedingly still-compression framework and highlight the upsides of those transforms and schemes in today's ways. The paper analyses DCT, KLT and HWT in the light of PSNR, compression extent and time intricacy and shows that DCT is the perfect among the considered transforms. The paper likewise exhibits that BPG is that the best scheme strengthened by its compression quality.