Abstract:
An input of an encoder receives moving image data comprising a sequence of frames to be encoded, each frame comprising a plurality of blocks in two dimensions with each block comprising a plurality of pixels in those two dimensions. A motion prediction module performs encoding by, for at least part of each of a plurality of said frames, coding each block relative to a respective reference portion of another frame of the sequence, with the respective reference portion being offset from the block by a respective motion vector. According to the present disclosure, the moving image data of this plurality of frames comprises a screen capture stream, and the motion prediction module is configured to restrict each of the motion vectors of the screen capture stream to an integer number of pixels in at least one of said dimensions.
Abstract:
Multi-layered rate control for scalable video coding is provided. A parameter value may be calculated based on a current layer target bit rate and a current layer buffer state for a frame in a video stream. The frame may include a lower layer and one or more higher layers. A determination may then be made as to whether the current layer is the lower layer. If the current layer is the lower layer, a determination may then be made as to whether a coupling request has been received from a higher layer in the frame. If the coupling request has been received from the higher layer in the frame, the parameter value for the current layer may be increased based on a buffer state threshold value of the higher layer in the frame.
Abstract:
Techniques and tools for video coding/decoding with sub-block transform coding/decoding and re-oriented transforms are described. For example, a video encoder adaptively switches between 8×8, 8×4, and 4×8 DCTs when encoding 8×8 prediction residual blocks; a corresponding video decoder switches between 8×8, 8×4, and 4×8 inverse DCTs during decoding. The video encoder may determine the transform sizes as well as switching levels (e.g., frame, macroblock, or block) in a closed loop evaluation of the different transform sizes and switching levels. When a video encoder or decoder uses spatial extrapolation from pixel values in a causal neighborhood to predict pixel values of a block of pixels, the encoder/decoder can use a re-oriented transform to address non-stationarity of prediction residual values.
Abstract:
The coded block parameters used to code blocks of image samples into structures called macroblocks are compressed more efficiently by exploiting the correlation between chrominance and luminance blocks in each macroblock. In particular, the coded block pattern for chrominance and luminance are combined into a single parameter for the macroblock and jointly coded with a single variable length code. To further enhance coding efficiency, the spatial coherence of coded block patterns can be exploited by using spatial prediction to compute predicted values for coded block pattern parameters.
Abstract:
Approaches to selection of motion vector (“MV”) precision during video encoding are presented. These approaches can facilitate compression that is effective in terms of rate-distortion performance and/or computational efficiency. For example, a video encoder determines an MV precision for a unit of video from among multiple MV precisions, which include one or more fractional-sample MV precisions and integer-sample MV precision. The video encoder can identify a set of MV values having a fractional-sample MV precision, then select the MV precision for the unit based at least in part on prevalence of MV values (within the set) having a fractional part of zero. Or, the video encoder can perform rate-distortion analysis, where the rate-distortion analysis is biased towards the integer-sample MV precision. Or, the video encoder can collect information about the video and select the MV precision for the unit based at least in part on the collected information.
Abstract:
Techniques and tools for video coding/decoding with sub-block transform coding/decoding and re-oriented transforms are described. For example, a video encoder adaptively switches between 8×8, 8×4, and 4×8 DCTs when encoding 8×8 prediction residual blocks; a corresponding video decoder switches between 8×8, 8×4, and 4×8 inverse DCTs during decoding. The video encoder may determine the transform sizes as well as switching levels (e.g., frame, macroblock, or block) in a closed loop evaluation of the different transform sizes and switching levels. When a video encoder or decoder uses spatial extrapolation from pixel values in a causal neighborhood to predict pixel values of a block of pixels, the encoder/decoder can use a re-oriented transform to address non-stationarity of prediction residual values.
Abstract:
Techniques and tools for video coding/decoding with motion resolution switching and sub-block transform coding/decoding are described. For example, a video encoder adaptively switches the resolution of motion estimation and compensation between quarter-pixel and half-pixel resolutions; a corresponding video decoder adaptively switches the resolution of motion compensation between quarter-pixel and half-pixel resolutions. For sub-block transform sizes, for example, a video encoder adaptively switches between 8×8, 8×4, and 4×8 DCTs when encoding 8×8 prediction residual blocks; a corresponding video decoder switches between 8×8, 8×4, and 4×8 inverse DCTs during decoding.
Abstract:
An improved loss recovery method for coding streaming media classifies each data unit in the media stream as an independent data unit (I unit), a remotely predicted unit (R unit) or a predicted data unit (P unit). Each of these units is organized into independent segments having an I unit, multiple P units and R units interspersed among the P units. The beginning of each segment is the start of a random access point, while each R unit provides a loss recovery point that can be placed independently of the I unit. This approach separates the random access point from the loss recovery points provided by the R units, and makes the stream more impervious to data losses without substantially impacting coding efficiency. The most important data units are transmitted with the most reliability to ensure that the majority of the data received by the client is usable. The I units are the least sensitive to transmission losses because they are coded using only their own data. While they provide the best coding efficiency, the P units are the most sensitive to data loss because the loss of one P unit renders useless all of the P units that depend on it. The remotely predicted units are dependent on the I unit, or in an alternative implementation, on another R unit.
Abstract:
An audio encoder implements multi-channel coding decision, band truncation, multi-channel rematrixing, and header reduction techniques to improve quality and coding efficiency. In the multi-channel coding decision technique, the audio encoder dynamically selects between joint and independent coding of a multi-channel audio signal via an open-loop decision based upon (a) energy separation between the coding channels, and (b) the disparity between excitation patterns of the separate input channels. In the band truncation technique, the audio encoder performs open-loop band truncation at a cut-off frequency based on a target perceptual quality measure. In multi-channel rematrixing technique, the audio encoder suppresses certain coefficients of a difference channel by scaling according to a scale factor, which is based on current average levels of perceptual quality, current rate control buffer fullness, coding mode, and the amount of channel separation in the source. In the header reduction technique, the audio encoder selectively modifies the quantization step size of zeroed quantization bands so as to encode in fewer frame header bits.
Abstract:
An improved loss recovery method for coding streaming media classifies each data unit in the media stream as an independent data unit (I unit), a remotely predicted unit (R unit) or a predicted data unit (P unit). Each of these units is organized into independent segments having an I unit, multiple P units and R units interspersed among the P units. The beginning of each segment is the start of a random access point, while each R unit provides a loss recovery point that can be placed independently of the I unit. This approach separates the random access point from the loss recovery points provided by the R units, and makes the stream more impervious to data losses without substantially impacting coding efficiency. The most important data units are transmitted with the most reliability to ensure that the majority of the data received by the client is usable. The I units are the least sensitive to transmission losses because they are coded using only their own data. While they provide the best coding efficiency, the P units are the most sensitive to data loss because the loss of one P unit renders useless all of the P units that depend on it. The remotely predicted units are dependent on the I unit, or in an alternative implementation, on another R unit.