In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

In one embodiment, an apparatus comprises a storage device and a processor. The storage device may store a plurality of compressed images comprising one or more compressed master images and one or more compressed slave images. The processor may: identify an uncompressed image; access context information associated with the uncompressed image and the one or more compressed master images; determine, based on the context information, whether the uncompressed image is associated with a corresponding master image; upon a determination that the uncompressed image is associated with the corresponding master image, compress the uncompressed image into a corresponding compressed image with reference to the corresponding master image; upon a determination that the uncompressed image is not associated with the corresponding master image, compress the uncompressed image into the corresponding compressed image without reference to the one or more compressed master images; and store the corresponding compressed image on the storage device.

A video processing method includes determining, for a conversion between a current block of a video and a bitstream representation of the video, whether to enable a level mapping operation or a level remapping operation based on a rule, wherein the level mapping operation or the level remapping operation includes changing between a first representation of a residual coefficient of the current block and a second representation of the residual coefficient of the current block based on neighboring residual coefficients of the residual coefficient; and performing the conversion by selectively using the level mapping operation or the level remapping operation based on the determining.

A video processing method includes determining, for a conversion between a current block of a video and a bitstream representation of the video, whether to enable a level mapping operation or a level remapping operation based on a rule, wherein the level mapping operation or the level remapping operation includes changing between a first representation of a residual coefficient of the current block and a second representation of the residual coefficient of the current block based on neighboring residual coefficients of the residual coefficient; and performing the conversion by selectively using the level mapping operation or the level remapping operation based on the determining.

System and method for improving the shaving experience by providing improved visibility of the skin shaving area. A digital camera is integrated with the electric shaver for close image capturing of shaving area, and displaying it on a display unit. The display unit can be integral part of the electric shaver casing, or housed in a separated device which receives the image via a communication channel. The communication channel can be wireless (using radio, audio or light) or wired, such as dedicated cabling or using powerline communication. A light source is used to better illuminate the shaving area. Video compression and digital image processing techniques are used for providing for improved shaving results. The wired communication medium can simultaneously be used also for carrying power from the electric shaver assembly to the display unit, or from the display unit to the electric shaver.

The disclosure relates to a method of processing a sequence of image frames to reduce its length. One implementation may involve extracting coefficients (e.g., Discrete Cosine Transform coefficients) from components of individual frames, and comparing the resulting coefficients for sequential frames to identify frames having the least change from a prior frame. Also, scene change values for each frame may be calculated and placed in a sorted list to facilitate identification of frames for removal. Frame removal may be conducted in rounds, where a group of pictures (GOP) may only have one frame removed for any given round.

The disclosure relates to a method of processing a sequence of image frames to reduce its length. One implementation may involve extracting coefficients (e.g., Discrete Cosine Transform coefficients) from components of individual frames, and comparing the resulting coefficients for sequential frames to identify frames having the least change from a prior frame. Also, scene change values for each frame may be calculated and placed in a sorted list to facilitate identification of frames for removal. Frame removal may be conducted in rounds, where a group of pictures (GOP) may only have one frame removed for any given round.

A method of decoding an image with latent feature-domain intra-prediction is performed by at least one processor and includes receiving a set of latent blocks and for each of the blocks in the set of latent blocks: predicting a block, based on a set of previously recovered blocks; receiving a selection signal indicating a currently recovered block, based on the selection signal performing one of (1) and (2): (1) generating a compact residual, a set of residual context parameters, a decoded residual, and generating a first decoded block; (2) generating a second decoded block, based on a compact representation block and a set of context parameters. The method further includes generating a set of recovered blocks comprising each of the currently recovered blocks; generating a recovered latent image by merging all the blocks in the set of recovered blocks; and decoding the recovered latent image, to obtain a reconstructed image.

Techniques for lossless compression of a digital image using prior image context.

Techniques for lossless compression of a digital image using prior image context.