Spatial/temporal information (SI/TI) is used to characterize the scene-complexity of videos. It is often hypothesized that SI/TI can also be used to measure the encoding-complexity of a video; although higher scene-complexity may not always mean a higher encoding-complexity for videos. In this paper, two alternative views on the video encoding-complexity are proposed. In the first view, a ground-truth of encoding-complexity is derived using the area-under-the-curve of a rate-quality plot. In the second view, constant rate-factor (CRF) video encoding is used to synthesize a measure of video encoding-complexity. Experimental evaluation on 734 UHD video sequences showed that the CRF based method, compared to SI/TI, yields better complexity characterization w.r.t. the ground-truth.

Recently, Netflix proposed a perceptual per-scene video encoding framework for over-the-top (OTT) adaptive streaming applications. Given a full-matrix of fixed quantization (fixed-QP) scene encodes (i.e., all scenes encoded at all QP values for all resolutions), it selects scene encodes which reduce the average bitrate for a target quality value. This paper proposes a low complexity alternative for achieving per-scene encoding. At first, using a small set of scene encodes, a bitrate-resolution-quality model of the complete video is constructed. Using this model, per-scene video encoding is posed as a global optimization problem. The proposed method adapts the instantaneous bitrate and encoding resolution to the complexity of the scene, enabling lower overall bitrate compared to the adaptive-QP approach [2]. Additionally, thanks to our bitrate-resolution-quality modeling, the proposed method significantly reduces the computational complexity without the need to compute a full-matrix of scene encodes and their respective quality computations.

Quality prediction performance of recently standardized parametric P.1203 models for real-streaming services: YouTube, Vimeo, Amazon Instant Video and a proprietary DASH-based streaming framework, is analyzed. In particular, a validation database comprising of bitstream traces from aforementioned services is used to evaluate the performance of P.1203 (mode 0 and mode 1) models. It is understood from the analysis that P.1203 models do capture the diverse encoding optimization strategies used in today's streaming applications to yield accurate prediction of long-term audio-visual quality, and the overall performance of P.1203 can be further enhanced using short-term Full Reference (FR) video-only models.

Quality variations due to network bandwidth fluctuations is a common phenomenon in today's HTTP based over-the-top (OTT) video streaming services where video sessions often last several minutes. In addition to quality variations, OTT video playouts encounter buffer under-runs resulting in client-side video interruptions (a.k.a. stallings) which highly impact viewer's quality of experience (QoE). OTT service providers would like to know the overall quality score of a given video stream to optimize their service. Network provider's target is to maximize the overall video quality score for their network. In any case an accurate measurement of video stream quality is a key requirement for different stake holders in the OTT business. This paper investigates the design of a parametric model for estimating long-term (60 sec or longer) perceptual quality from short-term (10 sec) video quality estimates in H.264 based adaptive video streaming. Based on an advanced subjective test framework, the proposed model linearly combines the perceptual impact of three types of distortions (initial-loading, stalling and coding/quality-switching) observed in OTT video in an overall quality score on a 5-point opinion scale. The proposed model yields high accuracy of quality prediction when evaluated for up-to 3 minutes long video sequences.

This paper presents a novel two-layer coding framework targeting visually lossless compression of screen content video. The proposed framework employs the conventional HEVC standard for the base-layer. For the enhancement layer, a hybrid of spatial and temporal block-prediction mechanism is introduced to guarantee a small energy of the error-residual. Spatial prediction is generally chosen for dynamic areas, while temporal predictions yield better prediction for static areas in a video frame. The prediction residual is quantized based on whether a given block is static or dynamic. Run-length coding, Golomb based binarization and context-based arithmetic coding are employed to efficiently code the quantized residual and form the enhancement-layer. Performance evaluations using 4:4:4 screen content sequences show that, for visually lossless video quality, the proposed system significantly saves the bit-rate compared to the two-layer lossless HEVC framework.

This paper proposes novel scalable mesh coding designs exploiting the intraband or composite statistical dependencies between the wavelet coefficients. A Laplacian mixture model is proposed to approximate the distribution of the wavelet coefficients. This model proves to be more accurate when compared to commonly employed single Laplacian or generalized Gaussian distribution models. Using the mixture model, we determine theoretically the optimal embedded quantizers to be used in scalable wavelet-based coding of semiregular meshes. In this sense, it is shown that the commonly employed successive approximation quantization is an acceptable, but in general, not an optimal solution. Novel scalable intraband and composite mesh coding systems are proposed, following an information-theoretic analysis of the statistical dependencies between the coefficients. The wavelet subbands are independently encoded using octree-based coding techniques. Furthermore, context-based entropy coding employing either intraband or composite models is applied. The proposed codecs provide both resolution and quality scalability. This lies in contrast to the state-of-the-art interband zerotree-based semiregular mesh coding technique, which supports only quality scalability. Additionally, the experimental results show that, on average, the proposed codecs outperform the interband state-of-the-art for both normal and nonnormal meshes. Finally, compared with a zerotree coding system, the proposed coding schemes are better suited for software/hardware parallelism, due to the independent processing of wavelet subbands.

In this paper, a novel wavelet-based composite mesh coding scheme is presented. In contrast to the state-of-the-art scalable interband mesh codec, the proposed codec relies on composite dependency models that capture both the interband and intraband statistical dependencies between wavelet coefficients. Additionally, each wavelet subband is processed independently, which allows for parallelized processing and for a progressive reconstruction of each mesh resolution level. Compared to the state-of-the-art, the proposed codec yields for almost all rate points superior compression performance in L2-sense. Furthermore, the generated bitstreams are near-optimal in rate-distortion sense, which eliminates the need of a post-compression rate-distortion optimization technique.