Heterogeneous analog-digital signal processing

Abstract

Signal processing method and apparatus, comprising: obtaining / generating an analogue (image, video, audio, LIDAR, RADAR, temperature, and / or volumetric) signal (possibly from a sensor 1702); processin

Description

Technical Field The present disclosure relates to heterogeneous analog-digital signal processing. Background Downscaling is a computationally costly process. In particular, downscaling accesses significant amounts of memory. Although non-linear downscaling processes minimise, or at least reduce, aliasing, ringing and loss of high frequencies, they are particularly computationally costly in this sense. Summary Various aspects of the present disclosure are set out in the appended claims. Further features and advantages will become apparent from the following description of preferred embodiments, given by way of example only, which is made with reference to the accompanying drawings. Brief Description of the Drawings Figure 1 shows a schematic block diagram of an example of a signal processing system; Figures 2A and 2B show schematic block diagrams of another example of a signal processing system; Figure 3 shows a schematic diagram of an example of a hierarchical data signal processing arrangement; Figure 4 shows a schematic diagram of another example of a hierarchical data signal processing arrangement; Figure 5 shows a schematic diagram of an example encoder for a hierarchical encoding scheme; Figure 6 shows a schematic diagram of a number of levels of quality within a first example hierarchical coding scheme; Figure 7 shows a schematic diagram of a number of levels of quality within a second example hierarchical coding scheme; Figure 8 shows a schematic diagram of an example of a bytestream structure for a frame; Figure 9 shows a schematic diagram of an example of a coding structure; Figure 10 shows a schematic block diagram of an example of a system for performing a statistical coding methodology; Figure 11 shows a schematic diagram of an example of pyramidal reconstruction; Figure 12 shows a schematic block diagram of an example of a signal processing system; Figure 13 shows a schematic block diagram of an example of a heterogeneous analog-digital signal processing system; Figure 14 shows a schematic block diagram heterogeneous analog-digital signal processing system; Figure 15 shows a schematic block diagram heterogeneous analog-digital signal processing system; Figure 16 shows a schematic block diagram heterogeneous analog-digital signal processing system; Figure 17 shows a schematic block diagram heterogeneous analog-digital signal processing system; Figure 18 shows a schematic block diagram of of of of of another another another another another example example example example example of of of of of heterogeneous analog-digital signal processing system; and Figure 19 shows a schematic block diagram of an example of an apparatus. Detailed Description Referring to Figure 1, there is shown an example of a signal processing system 100. The signal processing system 100 is used to process signals. Examples of types of signal include, but are not limited to, video signals, image signals, audio signals, volumetric signals such as those used in medical, scientific or holographic imaging, or other multidimensional signals. The signal processing system 100 includes a first apparatus 102 and a second apparatus 104. The first apparatus 102 and second apparatus 104 may have a clientserver relationship, with the first apparatus 102 performing the functions of a server device and the second apparatus 104 performing the functions of a client device. The signal processing system 100 may include at least one additional apparatus (not shown). The first apparatus 102 and / or second apparatus 104 may comprise one or more components. The one or more components may be implemented in hardware and / or software. The one or more components may be co-located or may be located remotely from each other in the signal processing system 100. Examples of types of apparatus include, but are not limited to, computerised devices, handheld or laptop computers, tablets, mobile devices, games consoles, smart televisions, set-top boxes, Extended Reality (XR) headsets (including Augmented Reality (AR) and / or Virtual Reality (VR) headsets) etc. The first apparatus 102 is communicatively coupled to the second apparatus 104 via a data communications network 106. Examples of the data communications network 106 include, but are not limited to, the Internet, a Local Area Network (LAN) and a Wide Area Network (WAN). The first and / or second apparatus 102, 104 may have a wired and / or wireless connection to the data communications network 106. In this example, the first apparatus 102 comprises an encoder 108. The encoder 108 is configured to encode data comprised in and / or derived based on the signal, which is referred to hereinafter as “signal data”. For example, where the signal is a video signal, the encoder 108 is configured to encode video data. Video data comprises a sequence of multiple images or frames. The encoder 108 may perform one or more further functions in addition to encoding signal data. The encoder 108 may be embodied in various different ways. For example, the encoder 108 may be embodied in hardware and / or software. The encoder 108 may encode metadata associated with the signal. The first apparatus 102 may use one or more than one encoder 108. Although in this example the first apparatus 102 comprises the encoder 108, in other examples the first apparatus 102 is separate from the encoder 108. In such examples, the first apparatus 102 is communicatively coupled to the encoder 108. The first apparatus 102 may be embodied as one or more software functions and / or hardware modules. In this example, the second apparatus 104 comprises a decoder 110. The decoder 110 is configured to decode signal data. The decoder 110 may perform one or more further functions in addition to decoding signal data. The decoder 110 may be embodied in various different ways. For example, the decoder 110 may be embodied in hardware and / or software. The decoder 110 may decode metadata associated with the signal. The second apparatus 104 may use one or more than one decoder 110. Although in this example the second apparatus 104 comprises the decoder 110, in other examples the second apparatus 104 is separate from the decoder 110. In such examples, the second apparatus 104 is communicatively coupled to the decoder 110. The second apparatus 104 may be embodied as one or more software functions and / or hardware modules. The encoder 108 encodes signal data and transmits the encoded signal data to the decoder 110 via the data communications network 106. The decoder 110 decodes the received, encoded signal data and generates decoded signal data. The decoder 110 may output the decoded signal data, or data derived using the decoded signal data. For example, the decoder 110 may output such data for display on one or more display devices associated with the second apparatus 104. The one or more display devices may be components of the second apparatus 104 or may otherwise be associated with the second apparatus 104. The one or more display devices may be operable to display XR content and may, therefore, be referred to as XR display devices. In some examples described herein, the encoder 108 transmits to the decoder 110 a representation of a signal at a given level of quality and information the decoder 110 can use to reconstruct a representation of some or all of the signal at one or more higher levels of quality. Such information may be referred to as “reconstruction data”. In some examples, “reconstruction” of a representation involves obtaining a representation that is not an exact replica of an original representation. The extent to which the representation is the same as the original representation may depend on various factors including, but not limited to, quantisation levels. A representation of a signal at a given level of quality may be considered to be a rendition, version or depiction of data comprised in the signal at the given level of quality. In some examples, the reconstruction data is included in the signal data that is encoded by the encoder 108 and transmitted to the decoder 110. For example, the reconstruction data may be in the form of metadata. In some examples, the reconstruction data is encoded and transmitted separately from the signal data. The information the decoder 110 uses to reconstruct the representation of the signal at the one or more higher levels of quality may comprise residual data, as described in more detail below. Residual data is an example of reconstruction data. The information the decoder 110 uses to reconstruct the representation of the signal at the one or more higher levels of quality may also comprise configuration data relating to processing of the residual data. The configuration data may indicate how the residual data has been processed by the encoder 108 and / or how the residual data is to be processed by the decoder 110. The configuration data may be signalled to the decoder 110, for example in the form of metadata. The first and / or second apparatuses 102, 104 may be configured to perform some or all of the techniques described herein. A computer program may be configured to perform some or all of the techniques described herein. Referring to Figures 2A and 2B, there is shown schematically an example of a signal processing system 200. The signal processing system 200 includes a first apparatus 202 and a second apparatus 204. In this example, the first apparatus 202 comprises an encoder and the second apparatus 204 comprises a decoder. However, as explained above, in other examples, the encoder is not comprised in the first apparatus 202 and / or the decoder is not comprised in the second apparatus 204. In each of the first apparatus 202 and the second apparatus 204, items are shown on two logical levels. The two levels are separated by a dashed line. Items on the first, highest level relate to data at a first level of quality. Items on the second, lowest level relate to data at a second level of quality. The first level of quality is higher than the second level of quality. The first and second levels of quality relate to a tiered hierarchy having multiple levels of quality. In some examples, the tiered hierarchy comprises more than two levels of quality. In such examples, the first apparatus 202 and the second apparatus 204 may include more than two different levels. There may be one or more other levels above and / or below those depicted in Figures 2 A and 2B. As described herein, in certain cases, the levels of quality may correspond to different spatial resolutions. Referring first to Figure 2A, the first apparatus 202 obtains a first representation of an image at the first level of quality 206. A representation of a given image is a representation of data comprised in the image. The image may be a given frame of a video. The first representation of the image at the first level of quality 206 will be referred to as “input data” hereinafter as, in this example, it is data provided as an input to the encoder in the first apparatus 202. The first apparatus 202 may receive the input data 206. For example, the first apparatus 202 may receive the input data 206 from at least one other apparatus. The first apparatus 202 may be configured to receive successive portions of input data 206, e.g. successive frames of a video, and to perform the operations described herein to each successive frame. For example, a video may comprise frames Fi, F2, ... Ft and the first apparatus 202 may process each of these in turn. The first apparatus 202 derives data 212 based on the input data 206. In this example, the data 212 based on the input data 206 is a representation 212 of the image at the second, lower level of quality. In this example, the data 212 is derived by performing a downsampling operation on the input data 206 and will therefore be referred to as “downsampled data” hereinafter. In other examples, the data 212 is derived by performing an operation other than a downsampling operation on the input data 206, or the data 212 is the same as the input data 206 (i.e. the input data 206 is not processed, e.g. downsampled). In this example, the downsampled data 212 is processed to generate processed data 213 at the second level of quality. In other examples, the downsampled data 212 is not processed at the second level of quality. As such, the first apparatus 202 may generate data at the second level of quality, where the data at the second level of quality comprises the downsampled data 212 or the processed data 213. In some examples, generating the processed data 213 involves the downsampled data 212 being encoded. Such encoding may occur within the first apparatus 202, or the first apparatus 202 may output the processed data 213 to an external encoder. Encoding the downsampled data 212 produces an encoded image at the second level of quality. The first apparatus 202 may output the encoded image, for example for transmission to the second apparatus 204. A series of encoded images, e.g. forming an encoded video, as output for transmission to the second apparatus 204 may be referred to as a “base” stream or “base” layer. As explained above, instead of being produced in the first apparatus 202, the encoded image may be produced by an encoder that is separate from the first apparatus 202. The encoded image may be part of an H.264 or H.265 encoded video, or otherwise. Generating the processed data 213 may, for example, comprise generating successive frames of video as output by a separate encoder such as an H.264 or H.265 video encoder. An intermediate set of data for the generation of the processed data 213 may comprise the output of such an encoder, as opposed to any intermediate data generated by the separate encoder. Generating the processed data 213 at the second level of quality may further involve decoding the encoded image at the second level of quality. The decoding operation may be performed to emulate a decoding operation at the second apparatus 204, as will become apparent below. Decoding the encoded image produces a decoded image at the second level of quality. In some examples, the first apparatus 202 decodes the encoded image at the second level of quality to produce the decoded image at the second level of quality. In other examples, the first apparatus 202 receives the decoded image at the second level of quality, for example from an encoder and / or decoder that is separate from the first apparatus 202. The encoded image may be decoded using an H.264 or H.265 decoder. The decoding by a separate decoder may comprise inputting encoded video, such as an encoded data stream configured for transmission to a remote decoder, into a separate black-box decoder implemented together with the first apparatus 202 to generate successive decoded frames of video. Processed data 213 may thus comprise a frame of video data that is generated via a complex non-linear encoding and decoding process, where the encoding and decoding process may involve modelling spatio-temporal correlations as per a particular encoding standard such as H.264 or H.265. However, because the output of any encoder is fed into a corresponding decoder, this complexity is effectively hidden from the first apparatus 202. In an example, generating the processed data 213 at the second level of quality further involves obtaining correction data based on a comparison between the downsampled data 212 and the decoded image obtained by the first apparatus 202, for example based on the difference between the downsampled data 212 and the decoded image. The correction data can be used to correct for encoder-decoder errors (which may also be referred to as “encode-decode errors”), namely errors introduced in encoding and decoding the downsampled data 212. In some examples, the first apparatus 202 outputs the correction data, for example for transmission to the second apparatus 204, as well as the encoded signal. This allows the recipient to correct for the encoder-decoder errors introduced in encoding and decoding the downsampled data 212. This correction data may also be referred to as a “first enhancement” stream. As the correction data may be based on the difference between the downsampled data 212 and the decoded image it may be seen as a form of residual data (e.g. that is different from the other set of residual data described later below). An item of residual data may be referred to as “a residual”. In some examples, generating the processed data 213 at the second level of quality further involves correcting the decoded image using the correction data. For example, the correction data as output for transmission may be placed into a form suitable for combination with the decoded image, and then added to the decoded image. This may be performed on a frame-by-frame basis. In other examples, rather than correcting the decoded image using the correction data, the first apparatus 202 uses the downsampled data 212. For example, in certain cases, just the encoded then decoded data may be used and, in other cases, encoding and decoding may be replaced by other processing. In some examples, generating the processed data 213 involves performing one or more operations other than the encoding, decoding, obtaining, and correcting acts described above. The first apparatus 202 obtains data 214 based on the data at the second level of quality. As indicated above, the data at the second level of quality may comprise the processed data 213, or the downsampled data 212 where the downsampled data 212 is not processed at the lower level. As described above, in certain cases, the processed data 213 may comprise a reconstructed video stream (e.g. from an encoding-decoding operation) that is corrected using correction data. In the example of Figures 2A and 2B, the data 214 is a second representation of the image at the first level of quality, the first representation of the image at the first level of quality being the input data 206. The second representation at the first level of quality may be considered to be a preliminary or predicted representation of the image at the first level of quality. In this example, the first apparatus 202 derives the data 214 by performing an upsampling operation on the data at the second level of quality. The data 214 will be referred to hereinafter as “upsampled data”. However, in other examples one or more other operations could be used to derive the data 214, for example where data 212 is not derived by downsampling the input data 206. The input data 206 and the upsampled data 214 are used to obtain residual data 216. The residual data 216 is associated with the image. The residual data 216 may be in the form of a set of residual elements, which may be referred to as a “residual frame” or a “residual image”. A residual element may be referred to as “a residual”. A residual element in the set of residual elements 216 may be associated with a respective image element in the input data 206. An example of an image element is a pixel. In this example, a given residual element is obtained by subtracting a value of an image element in the upsampled data 214 from a value of a corresponding image element in the input data 206. As such, the residual data 216 is useable in combination with the upsampled data 214 to reconstruct the input data 206. The residual data 216 may also be referred to as “reconstruction data” or “enhancement data”. In one case, the residual data 216 may form part of a “second enhancement” stream. The residual data 216 may therefore result from upsampler-downsampler asymmetry. Upsampler-downsampler asymmetry may also be referred to as “upsampling-downsampling asymmetry”, “upsample-downsample” asymmetry or the like. The first apparatus 202 obtains configuration data relating to processing of the residual data 216. The configuration data indicates how the residual data 216 has been processed and / or generated by the first apparatus 202 and / or how the residual data 216 is to be processed by the second apparatus 204. The configuration data may comprise a set of configuration parameters. The configuration data may be useable to control how the second apparatus 204 processes data and / or reconstructs the input data 206 using the residual data 216. The configuration data may relate to one or more characteristics of the residual data 216. The configuration data may relate to one or more characteristics of the input data 206. Different configuration data may result in different processing being performed on and / or using the residual data 216. The configuration data is therefore useable to reconstruct the input data 206 using the residual data 216. As described below, in certain cases, configuration data may also relate to the correction data described herein. In this example, the first apparatus 202 transmits to the second apparatus 204 data based on the downsampled data 212, data based on the residual data 216, and the configuration data (or data based on the configuration data), to enable the second apparatus 204 to reconstruct the input data 206. Turning now to Figure 2B, the second apparatus 204 receives data 220 based on (e.g. derived from) the downsampled data 212. The second apparatus 204 also receives data based on the residual data 216. For example, the second apparatus 204 may receive a “base” stream (data 220), a “first enhancement stream” (any correction data) and a “second enhancement stream” (residual data 216). The base stream may be referred to as a “base layer”. The first and / or second enhancement stream may be referred to as, and / or may be comprised in, an “enhancement layer”. The second apparatus 204 also receives the configuration data relating to processing of the residual data 216. The data 220 based on the downsampled data 212 may be the downsampled data 212 itself, the processed data 213, or data derived from the downsampled data 212 or the processed data 213. The data based on the residual data 216 may be the residual data 216 itself, or data derived from the residual data 216. In some examples, the received data 220 comprises the processed data 213, which may comprise the encoded image at the second level of quality and / or the correction data. In some examples, for example where the first apparatus 202 has processed the downsampled data 212 to generate the processed data 213, the second apparatus 204 processes the received data 220 to generate processed data 222. Such processing by the second apparatus 204 may comprise decoding an encoded image (e.g. that forms part of a “base” encoded video stream) to produce a decoded image at the second level of quality. In some examples, the processing by the second apparatus 204 comprises correcting the decoded image using obtained correction data. Hence, the processed data 222 may comprise a frame of corrected data at the second level of quality. In some examples, the encoded image at the second level of quality is decoded by a decoder that is separate from the second apparatus 204. The encoded image at the second level of quality may be decoded using an H.264 decoder. In other examples, the received data 220 comprises the downsampled data 212 and does not comprise the processed data 213. In some such examples, the second apparatus 204 does not process the received data 220 to generate processed data 222. The second apparatus 204 uses data at the second level of quality to derive the upsampled data 214. As indicated above, the data at the second level of quality may comprise the processed data 222, or the received data 220 where the second apparatus 204 does not process the received data 220 at the second level of quality. The upsampled data 214 is a preliminary representation of the image at the first level of quality. The upsampled data 214 may be derived by performing an upsampling operation on the data at the second level of quality. The second apparatus 204 obtains the residual data 216. The residual data 216 is useable with the upsampled data 214 to reconstruct the input data 206. The residual data 216 is indicative of a comparison between the input data 206 and the upsampled data 214. The second apparatus 204 also obtains the configuration data related to processing of the residual data 216. The configuration data is useable by the second apparatus 204 to reconstruct the input data 206. For example, the configuration data may indicate a characteristic or property relating to the residual data 216 that affects how the residual data 216 is to be used and / or processed, or whether the residual data 216 is to be used at all. In some examples, the configuration data comprises the residual data 216. There are several considerations relating to such processing. One such consideration is the amount of information that is generated, stored, transmitted and / or processed. The more information that is used, the greater the amount of resources that may be involved in handling such information. Examples of such resources include transmission resources, storage resources and processing resources. Some signal processing techniques allow a relatively small amount of information to be used. This may reduce the amount of data transmitted via the data communications network 106. The savings may be particularly relevant where the data relates to high quality video data, where the amount of information transmitted can be especially high. Another consideration is latency. Complex image processing may introduce latency, which may negatively impact performance. Other considerations include the ability of the decoder to perform image reconstruction accurately, reliably, and / or efficiently. Performing image reconstruction accurately and reliably may affect the ultimate visual quality of the displayed image and consequently may affect a viewer’s engagement with the image and / or with a video comprising the image. This can be especially relevant to XR. Efficient reconstruction is especially effective for mobile computing devices, which may readily be used in XR applications. Referring to Figure 3, there is shown schematically an example of a hierarchical data signal processing arrangement 300. The hierarchical data signal processing arrangement 300 represents multiple different levels of quality (LOQs). The levels of quality may relate to different levels of quality of data associated with the data signal. A factor that can be used to determine quality of image and / or video data is resolution. A higher resolution corresponds to a higher level of quality. The resolution may be spatial and / or temporal. Other factors that can be used to determine quality of image and / or video data include, but are not limited to, a level of quantization of the data, a level of frequency filtering of the data, peak signal-to-noise ratio of the data, a structural similarity (SSIM) index, etc. In this example, the hierarchical data signal processing arrangement 300 has three different layers (or ‘levels’), namely a first layer 302, a second layer 304 and a third layer 306. The hierarchical data signal processing arrangement could however have a different number of layers. The first layer 302 may be considered to be a base layer in that it represents a base level of quality and the second and third layers 304, 306 may be considered to be enhancement layers in that they represent enhancements in terms of quality over that associated with the base layer. The first layer 302 corresponds to a first level of quality LOQ1. The second layer 304 corresponds to a second level of quality LOQ2. The second level of quality LOQ2 is higher than the first level of quality LOQ1. The third layer 306 corresponds to a third level of quality LOQ2. The third level of quality LOQ3 is higher than the second level of quality LOQ2. The second level of quality LOQ2 is between (or ‘intermediate’) the first level of quality LOQ1 and the third level of quality LOQ3. In some examples, the hierarchical data signal processing arrangement 300 represents multiple different levels of quality of video data. For example, the first layer 302 may correspond to standard definition (SD) quality video, the second layer 304 may correspond to high definition (HD) quality video and the third layer 306 may correspond to ultra-high definition (UHD) video for example. Different combinations of different resolutions may be used, e.g. SD and UHD, HD and UHD, or SD, HD and UHD. In this example, each of the layers 302, 304, 306 is associated with respective enhancement data. Enhancement data may be used to generate data, such as image and / or video data, at a level of quality associated with the respective layer. Referring to Figure 4, there is shown schematically an optional variation of a hierarchical data signal processing arrangement 400. This may only be used for certain implementations and may not be used for typical implementations. The hierarchical data signal processing arrangement 400 shown in Figure 4 is similar to the hierarchical data signal processing arrangement 300 shown in Figure 3 in that it includes three layers 402, 404, 406. In this example, each of the layers 402, 404, 406 includes a set of sub-layers (or ‘sub-levels’). In this specific example, each of the layers includes four sub-layers. The first layer 402 is associated with a first level of quality LOQ1. Each of the sub-layers of the first layer 402 is associated with a respective level of quality. A first sub-layer of the first layer 402 is associated with level of quality LOQ11, a second sublayer of the first layer 402 is associated with level of quality LOQh, a third sub-layer of the first layer 402 is associated with level of quality LOQh and a fourth sub-layer of the first layer 402 is associated with level of quality LOQh. Similarly, the second layer 404 is associated with a second level of quality LOQ2, the third layer 406 is associated with a third level of quality LOQ3 and the sub-layers of the second and third layers 404, 406 are associated with respective, increasing levels of quality. The level of quality associated with layers and sub-layers higher in the hierarchical data signal processing arrangement 400 is higher than the level of quality associated with layers and sub-layers lower in the hierarchical data signal processing arrangement 400. As such, the level of quality increases from the bottom to the top of the hierarchical data signal processing arrangement 400. Some or all of the layers 402, 404, 406 could have a number of sub-layers other than four. Some of all of the layers 402, 404, 406 could have a different number of sublayers than each other. Some of the layers 402, 404, 406 may not have any sub-layers. In this example, each of the sub-layers is associated with respective enhancement data. Enhancement data may be used to generate data, such as image and / or video data, at a level of quality associated with the respective sub-layer. The hierarchical data signal processing arrangement 400 therefore comprises a first layer having a first set of sub-layers and a second layer having a second set of sublayers. Each of the sub-layers is associated with a respective level of quality and is associated with respective enhancement data. Using a hierarchical data signal processing arrangement such as the hierarchical data signal processing arrangement 400 may allow some devices to reconstruct at a specific layer, for example LOQ3, but only using say the first two sub-layers, LOQ3i and LOQ32, of that layer. This may be for efficiency, battery saving or limited capacity purposes. Using only some sub-layers may be considered to be partial reconstruction. Other devices may use all four of the sublayers in LOQ3, namely LOQ31, LOQ32, LOQ3s and LOQ34, and reconstruct the signal completely. Using all of the sub-layers may be considered to be full reconstruction. The reader is referred to UK patent application no. GB1603727.7, which describes a hierarchical arrangement in more detail. The entire contents of GB 1603727.7 are incorporated herein by reference. Figure 5 also shows 500 how a data plane of a data frame is received and is successively downsampled to generate a plurality of layers (0 to n are shown). The base layer may be a lowest layer and may represent an output of a last downsampling step (or a difference between that layer and a scalar offset). Higher layers are represented as residual data where an upsampled version of a lower layer is compared with an input (non-upsampled) version (e.g. following downsampling for layer Rn-i as shown), e.g. by subtracting a reconstructed upsampled version of a lower layer from the input version. Residual data allows efficient representation of the data frames, and may be particularly useful for sparse data where there may only be a few values within the residual data. On a decoding side the process may be reversed on receipt of encoded streams derived from Ro to Rn representing the layers. Figure 5 does not show encoding operations, which may comprise transforming, quantising and entropy encoding residual data. Some examples of hierarchical coding are set out in the SMPTE VC-6 standard, and the MPEG-5, Part-2, LCEVC standard (the specifications for both standards, including working drafts, being incorporated herein by reference). Additional descriptions of hierarchical coding may be found in one or more of U.S. Patent No. 8,977,065, filed on July 21, 2011, entitled “Inheritance in a tiered signal quality hierarchy,” the contents of which are hereby incorporated by reference in their entirety; U.S. Patent No. 8,948,248, filed on July 21, 2011, entitled “Tiered signal decoding and signal reconstruction,” the contents of which are hereby incorporated by reference in their entirety; U.S. Patent No. 8,711,943, filed on July 21, 2011, entitled “Signal processing and tiered signal encoding,” the contents of which are hereby incorporated by reference in their entirety; U.S. PatentNo. 9,129,411, filed on July 21, 2011, entitled “Upsampling in a tiered signal quality hierarchy,” the contents of which are hereby incorporated by reference in their entirety; and U.S. Patent No. 8,531,321, filed on July 21, 2011, entitled “Signal processing and inheritance in a tiered signal quality hierarchy,” the contents of which are hereby incorporated by reference in their entirety. In certain examples, in very constrained or public networks, streaming may be configured to use MPEG-5 Part 2 (LCEVC), as an alternative to the full-codec approach of SMPTE VC-6. For example, hierarchical encoding according to SMPTE VC-6 may be preferred for higher quality local transmissions where requirements are similar to video production (e.g. neighbouring rooms in a hospital or education establishment), but MPEG-5 Part 2 may be preferred for wider streaming over secure or unsecure networks (e.g. streaming over the Internet). Figure 6 shows 600 the approach of SMPTE VC-6, where there are a plurality of layers of quality going down to a lowest layer of quality, where all layers are encoded with a common codec. Figure 6 identifies layers 610-0, 610-1 and 610-n. Figure 7 shows 700 the approach of MPEG-5, Part 2, wherein there is a base codec and one or more layers that are encoded using a separate enhancement codec. When bandwidth is limited, MPEG-5, Part 2 may degrade to just use a base stream as generated by a base or legacy codec. This may be at a lower resolution to a full desired stream, wherein the enhancement levels or layers may allow a higher resolution. Instead of needing the base codec to encode 100% of the initial resolution, it may only need to encode 25% of the initial resolution, thus reducing the load on the base codec and allowing base encoded frames to be received even if resources are limited. If the enhancement codec is more efficient than the base codec, MPEG-5 Part 2 may allow a legacy codec to continue operating within its comfort zone while the quality at the higher resolution remains unaffected. Figure 7 shows a base codec 710, and two enhancement layers 720-1, 720-2. As shown in Figure 8, a bytestream 800 may include multiple fields, namely one or more headers and a payload. In general, a payload includes the actual data to be decoded, whilst the headers provide information needed when decoding the payload. The payload may include information about a plurality of planes. In other words, the payload is subdivided in portions, each portion corresponding to a plane. Each plane further comprises multiple sub-portions, each sub-portion associated with a level of quality. The logical structure of a Payload is an array of multi-tiered Tableaux, which precedes the Tile Tier with Tiles containing Residuals at their Top Layer. The data in the Payload that represents a Tessera is a a Stream. In the present example, Streams are ordered by LoQ, then by Plane, then by direction and then by Tier. However, the Streams can be ordered in any other way, for example first direction, then LoQ, the Plane, then Tier. The order between directions, LoQ and Planes can be done in any way, and the actual order can be inferred by using the information in the header, for example the stream offsets info. The payload contains a series of streams, each stream corresponding to an encoded tessera. For the purpose of this example, we assume that the size of a tessera is 16x16. First, the decoding module would derive a root tableau (for example, associated with a first direction of a first LoQ within a first plane). From the root tableau, the decoding module would derive up to 256 attributes associated with the corresponding up to 256 tesserae associated with it and which lie in the tier above the root tier (first tier). In particular, one of the attributes is the length of the stream associated with the tessera. By using said streamlengths, the decoding module can identify the individual streams and, if implemented, decode each stream independently. Then, the decoding module would derive, from each of said tessera, attributes associated with the 256 tesserae in the tier above (second tier). One of these attributes is the length of the stream associated with the tessera. By using said streamlengths, the decoding module can identify the individual streams and, if implemented, decode each stream independently. The process will continue until the top tier is reached. Once the top tier has been reached, the next stream in the bytestream would correspond to a second root tableau (for example, associated with a second direction of a first LoQ within a first plane), and the process would continue in the same way. The bytestream may include a fixed-sized header, i.e. a header whose byte / bit length is fixed. The header may include a plurality of fields. The fixed-sized header may include a first field indicating a version of the bytestream format (B.l - also described as format version : unit8). In an embodiment, this first field may include 8 bits (or equivalently 1 byte). This field may allow flexibility in the encoding / decoding process to use, adapt and / or modify the version of the bytestream format and inform a decoding module of said version. In this way, it is possible to use multiple different version of the encoding / decoding format and allow the decoding module to determine the correct version to be used. A decoding module would obtain said first field from the bytestream and determine, based on the value included in said first field, a version of the encoding format to be used in the decoding process of said bytestream. The decoding module may use and / or implement a decoding process to adapt to said version. The fixed-sized header may include a second field indicating a size of the picture frame encoded with a specific bytestream (B.2 - also described as picture size : unit32). The size of the picture frame may actually correspond to the size of the bytestream associated with that picture frame. In an embodiment, this first field may include 32 bits (or equivalently 4 bytes). The size of the picture frame may be indicated in units of bytes, but other units may be used. This allows the encoding / decoding process flexibility in encoding picture frames of different size (e.g., 1024x720 pixels, 2048x1540 pixels, etc.) and allow the decoding module to determine the correct picture frame size to be used for a specific bytestream. A decoding module would obtain said second field from the bytestream and determine, based on the value included in said second field, a size of a picture frame corresponding to said bytestream. The decoding module may use and / or implement a decoding process to adapt to said size, and in particular to reconstruct the picture frame from the encoded bytestream to fit into said size. The fixed-sized header may include a third field indicating a recommended number of bits / bytes to fetch / retrieve at the decoding module when obtaining the bytestream (B.3 - also described as recommended fetch size : unit32). In an embodiment, this first field may include 32 bits (or equivalently 4 bytes). This field may be particularly useful in certain applications and / or for certain decoding modules when retrieving the bytestream from a server, for example to enable the bytestream to be fetched / retrieved at the decoding module in “portions”. For example, this may enable partial decoding of the bytestream (as further described, for example, in European patent application No 17386047.9 filed on 6th December 2017 by the same applicant whose contents are included in their entirety by reference) and / or optimise the retrieval of the bytestream by the decoding module (as for example further described in European patent application No 12759221.0 filed on 20th July 2012 by the same applicant whose contents are included in their entirety by reference). A decoding module would obtain said third field from the bytestream and determine, based on the value included in said third field, a number of bits and / or bytes of the bytestream to be retrieved from a separate module (for example, a server and / or a content delivery network). The decoding module may use and / or implement a decoding process to request to the separate module said number of bits and / or bytes from the bytestream, and retrieve them from the separate module. The fixed-sized header may include another field indicating a generic value in the bytestream (B.3.1 - also described as element interpretation : uint8). In an embodiment, this first field may include 8 bits (or equivalently 1 byte). A decoding module would obtain said another field from the bytestream and determine, based on the value included in said another field, a value indicated by the field. The fixed-sized header may include a fourth field indicating various system information, including the type of transform operation to be used in the decoding process (B.4 - also described as pipeline : unit8). In an embodiment, this first field may include 8 bits (or equivalently 1 byte). A transform operation is typically an operation that transform a value from an initial domain to a transformed domain. One example of such a transform is an integer composition transform. Another example of such a transform is a composition transform. The composition transform (integer and / or standard) are further described in European patent application No. 13722424.2 filed on 13th May 2013 by the same applicant and incorporated herein by reference. A decoding module would obtain said fourth field from the bytestream and determine, based on at least one value included in said fourth field, a type of transform operation to be used in the decoding process. The decoding module may configure the decoding process to use the indicated transform operation and / or implement a decoding process which uses the indicated transform operation when converting one or more decoded transformed coefficient and / or value (e.g., a residual) into an original nontransform domain. The fixed-sized header may include a fifth field indicating a type of up-sampling filtering operation to be used in the decoding process (B.5 - also described as upsampler : unit8). In an embodiment, this first field may include 8 bits (or equivalently 1 byte). An up-sampling filtering operation comprises a filter which applies certain mathematical operations to a first number of samples / values to produce a second number of samples / values, wherein the second number is higher than the first number. The mathematical operations can either be pre-defined, adapted either based on an algorithm (e.g., using a neural network or some other adaptive filtering technique) or adapted based on additional information received at the decoding module. Examples of such up-sampling filtering operations comprise aNearestNeighbour filtering operation, a Sharp filtering operation, a Bi-cubic filtering operation, and a Convolutional Neural Network (CNN) filtering operations. These filtering operations are described in further detail in the present application, as well as in UK patent application No. 1720365.4 filed on 6th December 2017 by the same applicant and incorporated herein by reference. A decoding module would obtain said fifth field from the bytestream and determine, based on at least one value included in said fifth field, a type of up-sampling operation to be used in the decoding process. The decoding module may configure the decoding process to use the indicated up-sampling operation and / or implement a decoding process which uses the indicated up-sampling operation. The indication of the upsampling operation to be used allows flexibility in the encoding / decoding process, for example to better suit the type of picture to be encoded / decoded based on its characteristics. The fixed-sized header may include a sixth field indicating one or more modifying operations used in the encoding process when building the fixed-sized header and / or other headers and / or to be used in the decoding process in order to decode the bytestream (see below) (B.6 - also described as shortcuts : shortcuts t). These modifying operations are also called shortcuts. The general advantage provided by these shortcuts is to reduce the amount of data to be encoded / decoded and / or to optimise the execution time at the decoder, for example by optimising the processing of the bytestream. A decoding module would obtain said sixth field from the bytestream and determine, based on at least one value included in said sixth field, a type of shortcut used in the encoding process and / or to be used in the decoding process. The decoding module may configure the decoding process to adapt its operations based on the indicated shortcut and / or implement a decoding process which uses the indicated shortcut. The fixed-sized header may include a seventh field indicating a first number of bits to be used to represent an integer number and a second number of bits to be used to represent a fractional part of a number (B.7 - also described as element descriptor : tuple (uint5, utin3)). In an embodiment, this first field may include 8 bits (or equivalently 1 byte) subdivided in 5 bits for the first number of bits and 3 bits for the second number of bits. A decoding module would obtain said seventh field from the bytestream and determine, based on at least one value included in said seventh field, how many bits to dedicate to represent the integer part of a number that has both integer and fractional parts and how many bits to dedicate to a fractional number. The fixed-sized header may include an eighth field indicating a number of planes forming a frame and to be used when decoding the bytestream (B.8 - also described as num_plane : unit8). In an embodiment, this first field may include 8 bits (or equivalently 1 byte). A plane is defined in the present application and is, for example, one of the dimensions in a color space, for examples the luminance component Y in a YUV space, or the red component R in an RGB space. A decoding module would obtain said eighth field from the bytestream and determine, based on at least one value included in said fifth field, the number of planes included in a picture. The fixed-sized header may include a ninth field indicating a size of an auxiliary header portion included in a separate header - for example the First Variable-Size Header or the Second Variable-Size Header (B.9 - also described as auxheadersize : uuntl6). In an embodiment, this first field may include 16 bits (or equivalently 2 byte). This field allows the encoding / decoding process to be flexible and define potential additional header fields. A decoding module would obtain said ninth field from the bytestream and determine, based on at least one value included in said ninth field, a size of an auxiliary header portion included in a separate header. The decoding module may configure the decoding process to read the auxiliary header in the bytestream. The fixed-sized header may include a tenth field indicating a number of auxiliary attributes (B.10 - also described as num aux tile attribute : uint4 and numauxtableauattribute : uint4). In an embodiment, this first field may include 8 bits (or equivalently 1 byte) split into two 4-bits sections. This field allows the encoding / decoding process to be flexible and define potential additional attributes for both Tiles and Tableaux. These additional attributes may be defined in the encoding / decoding process. A decoding module would obtain said tenth field from the bytestream and determine, based on at least one value included in said tenth field, a number of auxiliary attributes associated with a tile and / or a number of auxiliary attributes associated with a tableau. The decoding module may configure the decoding process to read said auxiliary attributes in the bytestream. The bytestream may include a first variable-sized header, i.e. a header whose byte / bit length is changeable depending on the data being transmitted within it. The header may include a plurality of fields. The first variable-sized header may include a first field indicating a size of a field associated with an auxiliary attribute of a tile and / or a tableau (C. 1 - also described as aux attribute sizes : until6[num_aux_tile_attribute + num aux tableau attribute]). In an embodiment, the second field may include a number of sub-fields, each indicating a size for a corresponding auxiliary attribute of a tile and / or a tableau. The number of these sub-fields, and correspondingly the number of auxiliary attributes for a tile and / or a tableau, may be indicated in a field of a different header, for example the fixed header described above, in particular in field B.10. In an embodiment, this first field may include 16 bits (or equivalently 2 bytes) for each of the auxiliary attributes. Since the auxiliary attributes may not be included in the bytestream, this field would allow the encoding / decoding process to define the size of the auxiliary attributes were they to be included in the bytestream. This contrasts, for example, with the attributes (see for example C.2 below) which typically are pre-defined in size and therefore their size does not need to be specified and / or communicated. A decoding module would obtain said first field from the bytestream and determine, based on a value included in said first field, a size of an auxiliary attribute associated with a tessera, (i.e., either a tile or a tableau). In particular, the decoding module may obtain from said first field in the bytestream, a size of an auxiliary attribute for each of the auxiliary attributes which the decoding module is expecting to decode, for example based on information received separately about the number of auxiliary attributes to be specified. The decoding module may configure the decoding process to read the auxiliary attributes in the bytestream. The first variable-sized header may include a second field indicating, for each attribute of a tile and / or a tableau, a number of different versions of the respective attribute (C.2 - also described as nums attribute : until6[4 + num aux tile attribute + num aux tableau attribute]). The second field may include a number of sub-fields, each indicating for a corresponding attribute a number of different version of said respective attribute. The number of these sub-fields, and correspondingly the number of standard attributes and auxiliary attributes for a tile and / or a tableau, may be indicated at least in part in a field of a different header, for example the fixed header described above, in particular in field B.10. The attributes may comprise both standard attributes associated with a tile and / or a tableau and the auxiliary attributes as described above. In an embodiment, there are three standard attributes associated with a tile (e.g., Residual Statistics, T-Node Statistics and Quantization Parameters) and two standard attributes associated with a tableau (e.g., Streamlengths Statistics and T-Node Statistics). In an embodiment, since the T-Node Statistics for the tiles and the tableaux may be the same, they may only require to be specified once. In such embodiment, only four different standard attributes will need to be included (and therefore only four sub-fields, C.2.1 to C.2.4, each associated with one of the four standard attributes Residual Statistics, T-Node Statistics, Quantization Parameters and Streamlengths Statistics, are included in the second field, each indicating a number of different versions of the respective attribute). Accordingly, there may be four different sub-fields in said second field, each indicating the number of standard attributes for a tile and / or a tableau which need to be specified for the decoding process. By way of example, if the sub-field associated with the T-Node Statistics indicate a number 20, it means that there will be 20 different available versions of T-Node Statistics to use for tiles and / or attributes. A decoding module would obtain said second field from the bytestream and determine, based on a value included in said second field, a number of different versions of a respective attribute, said attribute associated with a tile and / or a tableau. The decoding module may configure the decoding process to use the available versions of the corresponding attributes. The first variable-sized header may include a third field indicating a number of different groupings of tiles, wherein each grouping of tiles is associated with a common attribute (C.3 - also described as num tileset: uintl6). In an embodiment, this first field may include 16 bits (or equivalently 2 bytes). In an embodiment, the common attribute may be the T-Node Statistics for a tile. For example, if a grouping of tiles (also known as “tileset”) is associated with the same T-node Statistics, it means that all the tiles in that grouping shall be associated with the same T-Node Statistics. The use of grouping of tiles sharing one or more common attributes allows the coding and decoding process to be flexible in terms of specifying multiple versions of a same attribute and associate them with the correct tiles. For example, if a group of tiles belongs to “Group A”, and “Group A” is associated with “Attribute A” (for example, a specific T-Node Statistics), then all the tiles in Group A shall use that Attribute A. Similarly, if a group of tiles belongs to “Group B”, and “Group B” is associated with “Attribute B” (for example, a specific T-Node Statistics different from that of Group A), then all the tiles in Group B shall use that Attribute B. This is particularly useful in allowing the tiles to be associated with a statistical distribution as close as possible to that of the tile but without having to specify different statistics for every tile. In this way, a balance is reached between optimising the entropy encoding and decoding (optimal encoding and decoding would occur if the distribution associated with the tile is the exact distribution of that tile) whilst minimising the amount of data to be transmitted. Tiles are grouped, and a “common” statistics is used for that group of tiles which is as close as possible to the statistics of the tiles included in that grouping. For example, if we have 256 tiles, in an ideal situation we would need to send 256 different statistics, one for each of the tiles, in order to optimise the entropy encoding and decoding process (an entropy encoder / decoder is more efficient the more the statistical distribution of the encoded / decoded symbols is close to the actual distribution of said symbols). However, sending statistics is impractical and expensive in terms of compression efficiency. So, typical systems would send only one single statistics for all the 256 tiles. However, if the tiles are grouped into a limited number of groupings, for example 10, with each tile in each grouping having similar statistics, then only 10 statistics would need to be sent. In this way, a better encoding / decoding would be achieved than if only one common statistics was to be sent for all the 256 tiles, whilst at the same time sending only 10 statistics and therefore not compromising too much the compression efficiency. A decoding module would obtain said third field from the bytestream and determine, based on a value included in said third field, a number of different groupings of tiles. The decoding module may configure the decoding process to use, when decoding a tile corresponding to a specific grouping, one or more attributes associated with said grouping. The first variable-sized header may include a fourth field indicating a number of different groupings of tableaux, wherein each grouping of tableaus is associated with a common attribute (C.4 - also described as num tableauset : uintl6). In an embodiment, this fourth field may include 16 bits (or equivalently 2 bytes). This field works and is based on the same principles as the third field, except that in this case it refers to tableaux rather than tiles. A decoding module would obtain said fourth field from the bytestream and determine, based on a value included in said fourth field, a number of different groupings of tableaux. The decoding module may configure the decoding process to use, when decoding a tableau corresponding to a specific grouping, one or more attributes associated with said grouping. The first variable-sized header may include a fifth field indicating a width for each of a plurality of planes (C.5 - also described as widths : uintl6[num_plane]). In an embodiment, this fifth field may include 16 bits (or equivalently 2 bytes) for each of the plurality of planes. A plane is further defined in the present specification, but in general is a grid (usually a two-dimensional one) of elements associated with a specific characteristic, for example in the case of video the characteristics could be luminance, or a specific color (e.g. red, blue or green). The width may correspond to one of the dimensions of a plane. Typically, there are a plurality of planes. A decoding module would obtain said fifth field from the bytestream and determine, based on a value included in said fifth field, a first dimension associated with a plane of elements (e.g., picture elements, residuals, etc.). This first dimension may be the width of said plane. The decoding module may configure the decoding process to use, when decoding the bytestream, said first dimension in relation to its respective plane. The first variable-sized header may include a sixth field indicating a width for each of a plurality of planes (C.6 - also described as heights : uintl6[num_plane]). In an embodiment, this sixth field may include 16 bits (or equivalently 2 bytes) for each of the plurality of planes. The height may correspond to one of the dimensions of a plane. A decoding module would obtain said sixth field from the bytestream and determine, based on a value included in said sixth field, a second dimension associated with a plane of elements (e.g., picture elements, residuals, etc.). This second dimension may be the height of said plane. The decoding module may configure the decoding process to use, when decoding the bytestream, said second dimension in relation to its respective plane. The first variable-sized header may include a seventh field indicating a number of encoding / decoding levels for each of a plurality of planes (C.7 - also described as num loqs : uint8[num_plane]). In an embodiment, this seventh field may include 16 bits (or equivalently 2 bytes) for each of the plurality of planes. The encoding / decoding levels correspond to different levels (e.g., different resolutions) within a hierarchical encoding process. The encoding / decoding levels are also referred in the application as Level of Quality. A decoding module would obtain said seventh field from the bytestream and determine, based on a value included in said seventh field, a number of encoding levels for each of a plurality of planes (e.g., picture elements, residuals, etc.). The decoding module may configure the decoding process to use, when decoding the bytestream, said number of encoding levels in relation to its respective plane. The first variable-sized header may include an eighth field containing information about the auxiliary attributes (C.8 - also described as aux header : uint8[aux_header_size]). In an embodiment, this eight field may include a plurality of 8 bits (or equivalently 1 byte) depending on a size specified, for example, in a field of the fixed header (e.g., B.9) A decoding module would obtain said eighth field from the bytestream and determine information about the auxiliary attributes. The decoding module may configure the decoding process to use, when decoding the bytestream, said information to decode the auxiliary attributes. The bytestream may include a second variable-sized header, i.e. a header whose byte / bit length is changeable depending on the data being transmitted within it. The header may include a plurality of fields. The second variable-sized header may include a first field containing, for each attribute, information about one or more statistics associated with the respective attribute (see D.l). The number of statistics associated with a respective attribute may be derived separately, for example via field C.2 as described above. The statistics may be provided in any form. In an embodiment of the present application, the statistics is provided using a particular set of data information which includes information about a cumulative distribution function (type residual stat t). In particular, a first group of sub-fields in said first field may contain information about one or more statistics associated with residuals values (also D.l.l -also described as residual stats : residual_stat_t[nums_attribute[O]]). In other words, the statistics may identify how a set of residual data are distributed. The number of statistics included in this first group of sub-fields may be indicated in a separate field, for example in the first sub-field C.2.1 of field C.2 as described above (also indicated as nums_attribute[O]). For example, if nums_attribute[O] is equal to 10, then there would be 10 different residuals statistics contained in said first field. For example, the first 10 sub-fields in the first field correspond to said different 10 residuals statistics. A second group of sub-fields in said first field may contain information about one or more statistics associated with nodes within a Tessera (also D.l.2 - also described as tnode stats : tnode_stat_t[nums_attribute[l]]). In other words, the statistics may identify how a set of nodes are distributed. The number of statistics included in this second group of sub-fields may be indicated in a separate field, for example in the second sub-field C.2.2 of field C.2 as described above (also indicated as nums_attribute[l]). For example, if nums_attribute[l] is equal to 5, then there would be 5 different t-node statistics contained in said first field. For example, considering the example above, after the first 10 sub-fields in the first field, the next 5 sub-fields correspond to said 5 different t-node statistics. A third group of sub-fields in said first field may contain information about one or more quantization parameters (also D.1.3 - also described as quantization_parameters : quantization_parameters_t[nums_attribute[2]]). The number of quantization parameters included in this third group of sub-fields may be indicated in a separate field, for example in the third sub-field C.2.3 of field C.2 as described above (also indicated as nums_attribute[2]). For example, if nums_attribute[2] is equal to 10, then there would be 10 different quantization parameters contained in said first field. For example, considering the example above, after the first 15 sub-fields in the first field, the next 10 sub-fields correspond to said 10 different quantization parameters. A fourth group of sub-fields in said first field may contain information about one or more statistics associated with streamlengths (also D.1.4 - also described as stream length stats : stream_length_stat_t[nums_attribute[3]]). In other words, the statistics may identify how a set of streamlengths are distributed. The number of statistics included in this fourth group of sub-fields may be indicated in a separate field, for example in the fourth sub-field C.2.4 of field C.2 as described above (also indicated as nums_attribute[3]). For example, if nums_attribute[4] is equal to 12, then there would be 12 different streamlengths statistics contained in said first field. For example, considering the example above, after the first 25 sub-fields in the first field, the next 12 sub-fields correspond to said 12 different streamlengths statistics. Further groups of sub-fields in said first field may contain information about auxiliary attributes (also described as aux atttributes : uintl[aux_attributes_size[i]] [num aux tile attribute + num aux tableau attribute]). The number of auxiliary attributes may be indicated in another field, for example in field C.2 as described above. Specifying one or more versions of the attributes (e.g., statistics) enables flexibility and accuracy in the encoding and decoding process, because for instance more accurate statistics can be specified for a specific grouping of tesserae (tiles and / or tableaux), thus making it possible to encode and / or decode said groupings in a more efficient manner. A decoding module would obtain said first field from the bytestream and determine, based on the information contained in said first field, one or more attributes to be used during the decoding process. The decoding module may store the decoded one or more attributes for use during the decoding process. The decoding module may, when decoding a set of data (for example, a tile and / or a tableau) and based on an indication of attributes to use in relation to that set of data, retrieve the indicated attributes from the stored decoded one or more attributes and use it in decoding said set of data. The second variable-sized header may include a second field containing, for each of a plurality of grouping of tiles, an indication of a corresponding set of attributes to use when decoding said grouping (D.2 - also described as tilesets : uintl6[3 + num aux tile attributes] [num tiles]). The number of groupings of tiles may be indicated in a separate field, for example in field C.3 described above. This second field enables the encoding / decoding process to specify which of the sets of attributes indicated in field D.l described above is to be used when decoding a tile. A decoding module would obtain said second field from the bytestream and determine, based on the information contained in said second field, which of a set of attributes is to be used when decoding a respective grouping of tiles. The decoding module would retrieve from a repository storing all the attributes the ones indicated in said second field, and use them when decoding the respective grouping of tiles. The decoding process would repeat said operations when decoding each of the plurality of grouping of tiles. By way of example, and using the example described above in relation to field D.l, let’s assume that for a first grouping of tiles the set of attributes indicated in said second field corresponds to residuals statistics No. 2, t_node statistics No. 1 and to quantization parameter No. 4 (we assume for simplicity that there are no auxiliary attributes). When the receiving module receives said indication, it would retrieve from the stored attributes (as described above) the second residuals statistics from the 10 stored residuals statistics, the first tnode statistics from the 5 stored tnode statistics and the fourth quantization parameter from the 10 stored quantization parameters. The second variable-sized header may include a fourth field containing, for each of a plurality of grouping of tableaux, an indication of a corresponding set of attributes to use when decoding said grouping (D.4 - also described as tableausets : uintl6[2 + num aux tableaux attributes] [num tableaux]). The number of groupings of tableaux may be indicated in a separate field, for example in field C.4 described above. This fourth field enables the encoding / decoding process to specify which of the sets of attributes indicated in field D. 1 described above is to be used when decoding a tableau. The principles and operations behind this fourth field corresponds to that described for the second field, with the difference that in this case it applies to tableaux rather than tiles. In particular, a decoding module would obtain said fourth field from the bytestream and determine, based on the information contained in said fourth field, which of a set of attributes is to be used when decoding a respective grouping of tableaux. The decoding module would retrieve from a repository storing all the attributes the ones indicated in said fourth field, and use them when decoding the respective grouping of tableaux. The decoding process would repeat said operations when decoding each of the plurality of grouping of tableaux. The second variable-sized header may include a fifth field containing, for each plane, each encoding / decoding level and each direction, an indication of a corresponding set of attributes to use when decoding a root tableau (D.5 - also described as root tableauset indices : uintl6[loq_idx][num_planes][4]). This fifth field enables the encoding / decoding process to specify which of the sets of attributes indicated in field D.l described above is to be used when decoding a root tableau. A decoding module would obtain said fifth field from the bytestream and determine, based on the information contained in said fifth field, which of a set of attributes is to be used when decoding a respective root tableau. The decoding module would retrieve from a repository storing all the attributes the ones indicated in said fifth field, and use them when decoding the respective grouping of tiles. In this way, the decoding module would effectively store all the possible attributes to be used when decoding tiles and / or tableaux associated with that bytestream, and then retrieve for each of a grouping of tiles and / or tableaux only the sub-set of attributes indicated in said second field to decode the respective grouping of tiles and / or tableaux. The second variable-sized header may include a third field containing information about the statistics of the groupings of tiles (D.3 - also described as cdf tilesets : line_segments_cdfl5_t<tilese_index_t>). The statistics may provide information about how many times a certain grouping of tiles occurs. The statistics may be provided in the form of a cumulative distribution function. In the present application, the way the cumulative distribution function is provided is identified as a function type, specifically type line_segments_cdfl5_t<x_axis_type>. By using said statistics, the encoding / decoding process is enabled to compress the information about the grouping of tiles (e.g., the indices of tiles) and therefore optimise the process. For example, if there are N different groupings of tiles, and correspondingly N different indexes, rather than transmitting these indexes in an uncompressed manner, which would require 2flog2N] (where [ ] is a ceiling function), the grouping can be compressed using an entropy encoder thus reducing significantly the number of bits required to communicate the groupings of tiles. This may represent a significant savings. For example, assume that there are 10,000 tiles encoded in the bytestream, and that these tiles are divided in 100 groupings. Without compressing the indexes, an index needs to be sent together with each tile, meaning that at least 2Vog210°l=7 bits per tile, which means a total of 70,000 bits. If instead the indexes are compressed using an entropy encoder to an average of 1.5 bits per index, the total number of bits to be used would be 15,000, reducing the number of bits to be used by almost 80%. A decoding module would obtain said third field from the bytestream and determine, based on the information contained in said third field, statistical information about the groupings of tiles. The decoding module would use said statistical information when deriving which grouping a tile belongs to. For example, the information about the tile grouping (e.g., tileset index) can be compressed using said statistics and then reconstructed at the decoder using the same statistics, for example using an entropy decoder. The second variable-sized header may include a sixth field containing information about the statistics of the groupings of tableaux (D.6 - also described as cdf tableausets : line_segments_cdfl5_t<tableauset_index_t>). The statistics may provide information about how many times a certain grouping of tableaux occurs. The statistics may be provided in the form of a cumulative distribution function. This field works in exactly the same manner as the third field but for grouping of tableaux rather than grouping of tiles. In particular, a decoding module would obtain said sixth field from the bytestream and determine, based on the information contained in said sixth field, statistical information about the groupings of tableaux. The decoding module would use said statistical information when deriving which grouping a tableau belongs to. For example, the information about the tableau grouping (e.g., tableauset index) can be compressed using said statistics and then reconstructed at the decoder using the same statistics, for example using an entropy decoder. The second variable-sized header may include a seventh field containing, for each plane, each encoding / decoding level and each direction, an indication of a location, within a payload of the bytestream, of one or more sub-streams (e.g., a Surface) of bytes associated for that respective plane, encoding / decoding level and direction (D.7 - also described as rootstreamoffsets root_stream_offset_t[loq_idx][num_planes][4]). The location may be indicated as an offset with respect to the start of the payload. By way of example, assuming 3 planes, 3 encoding / decoding levels and 4 directions, there will be 3*3*4=36 different substreams, and correspondingly there will be 36 different indication of locations (e.g., offsets). A decoding module would obtain said seventh field from the bytestream and determine, based on the information contained in said seventh field, where to find in the payload a specific sub-stream. The sub-stream may be associated with a specific direction contained in a specific plane which is within a specific encoding / decoding level. The decoding module would use said information to locate the sub-stream and decode said sub-stream accordingly. The decoding module may implement, based on this information, decoding of the various sub-stream simultaneously and / or in parallel. This can be advantageous for at least two reasons. First, it would allow flexibility in ordering of the sub-streams. The decoder could reconstruct, based on the location of the sub-streams, to which direction, plane and encoding / decoding level the sub-stream belongs to, without the need for that order to be fixed. Second, it would enable the decoder to decode the sub-streams independently from one another as effectively each sub-stream is separate from the others. The second variable-sized header may include an eighth field containing, for each plane, each encoding / decoding level and each direction, a size of the Stream of bytes associated with the root tableau (D.8 - also described as root stream lengths : root_stream_length_t[loq_idx][num_planes][4]). A decoding module would obtain said eighth field from the bytestream and determine, based on the information contained in said eighth field, the length of a stream associated with a root tableau. Figure 9 shows a more general view of an example coding structure 900. In particular, and as already described in the present application and / or in other patent applications by the same applicant (such as European Patent No. 17386046.1 filed on 6th December 2017 and incorporated herein by reference), there are various Tiers in the coding structure, with Tier 0 being formed by Tiles, Tier -m formed of Tableaux and Root Tier formed of the single Root Tier. For example, one can see that a Summit, corresponding to maximum Grid size that could be encoded, is present both at a specific Tier (i.e., the union of all the values in Layer 0 of each Tier) or could also be seen for each tessera (i.e., the 256 values in Layer 0 of each tessera). Another important concept is that of Active Volume which effectively corresponds to that portion of the coding structure associated with the area occupied by the Surface. In other words, all those tesserae which are associated with at least one element of the Surface belongs to the Active Volume. The Surface, on the other hand, corresponds to the area of the Summit where there are actual data (e.g., residuals or metadata) to be used. Figure 10 is a block diagram of a system 1000. In Figure 10, there is shown the system 1000, the system 1000 comprising a streaming server 1002 connected via a network 1014 to a plurality of client devices 1030, 1032. The streaming server 1002 comprising an encoder 1004, the encoder configured to receive and encode a first video stream utilising the methodology described herein. The streaming server 1004 is configured to deliver an encoded video stream 1006 to a plurality of client devices such as set-top boxes smart TVs, smartphones, tablet computers, laptop computers etc., 1030 and 1032. Each client device 1030 and 1032 is configured to decode and render the encoded video stream 1006. The client devices and streaming server 1004 are connected via a network 1014. For ease of understanding the system 1000 of Figure 10 is shown with reference to a single streaming server 1002 and two recipient client devices 1030, 1032 though in further embodiments the system 1000 may comprise multiple servers (not shown) and several tens of thousands of client devices. The streaming server 1002 can be any suitable data storage and delivery server which is able to deliver encoded data to the client devices over the network. Streaming servers are known in the art, and use unicast and / or multicast protocols. The streaming server is arranged to encode and store the encoded data stream, and provide the encoded video data in one or more encoded data streams 1006 to the client devices 1030 and 1032. The encoded video stream 1006 is generated by the encoder 1004. The encoder 1004 in Figure 10 is located on the streaming server 1002, though in further embodiments the encoder 1004 is located elsewhere in the system 1000. The encoder 1004 generates the encoded video stream utilising the techniques described herein. The encoder further comprises a statistical module 1008 configured to determine and calculate statistical properties of the video data. The client devices 1030 and 1032 are devices known in the art and comprise the known elements required to receive and decode a video stream such as a processor, communications port and decoder. A Level of Quality (LoQ) represents Pels (elements of a picture) encoded at a certain resolution. With reference to Figure 11, Four Cycles of Pyramidal Reconstruction for Initial LoQ and three higher quality successor LoQs. On all higher Cycles, Residuals are dequantized and Composed prior to adding a predicted picture to obtain a Reconstructedlmage. The predicted picture comes from upsampling the Reconstructedlmage belonging to the previous LoQ. (The Initial Cycle differs since it does not add any predicted picture.) Figure 11 shows schematically 1100 that higher LoQs are predicted from lower LoQs and are then corrected using Residuals. The process leading from one LoQ to its successor, or leading to an Initial LoQ, is called a Cycle. In Figure 11 the use of Tiers of Tesserae, discussed elsewhere, to decode Residuals is not emphasized, but is represented by the rectangles entitled “De-sparsification” and “Entropy Decoding”. Every successor LoQ, LoQ -n+1, has a Reconstructedlmage that is generated from an upsampled version of the Reconstructedlmage from LoQ -n (the prediction data) and from decoding and applying a Composition transform to additional Residual data. Appropriate Residual data is contained in the Residual Surfaces connected with the successor LoQ. This recursive approach is called Pyramidal Reconstruction. To initiate the process, the Initial LoQ is decoded first. (Note: The highest Level of Quality is LoQ 0, the next highest LoQ -1, etc.). Within an LoQ, the Composition Transform allows a single ComposedResidualArray of Pels to be recovered from 4 ResidualSurfaces of transformed Pels. Referring to Figure 12, there is shown a schematic block diagram of an example of a signal processing system 1200. The signal processing system 1200 may be used to implement various heterogeneous analog-digital signal processing methods. The expression “heterogeneous analog-digital signal processing” is used herein to mean signal processing in analog and digital domains. Such methods may alternatively be referred to simply as “signal processing” methods. The example signal processing system 1200 comprises a sensor 1202. For example, the sensor 1202 may comprise a thermal sensor, a microphone, a camera, and / or any other type of sensor. An analog signal is obtained from the sensor 1202. The part of the example signal processing system 1200 that corresponds to the analog domain 1204 is shown in Figure 12 using a dashed box. In this example, the analog domain 1204 comprises the sensor 1202. A domain may also be referred to as a “space”. The example signal processing system 1200 comprises an analog-to-digital converter (ADC) 1206. The ADC 1206 converts an analog signal to a digital signal by digitising the analog signal. The part of the example signal processing system 1200 that corresponds to the digital domain 1208 is shown in Figure 12 using a dashed box. In this example, the digital domain 1208 comprises a downscaler 1210, an encoder-decoder 1212, an upscaler 1214, a residual generator 1216, and a residual encoder 1218. The digital domain 1208 may comprise other components not shown in Figure 12. For example, the digital domain 1208 may comprise a transform component. A downscaler may be referred to as a “downsampler”. An upscaler may be referred to as an “upsampler”. In this example, the digital domain 1208 generates correction data 1220, an encoded base signal 1222, and encoded residuals 1224. In this example, the ADC 1206 straddles the analog and digital domains 1204, 1208. In use, the sensor 1202 outputs an analog signal to the ADC 1206. The ADC 1206 converts the analog signal into a digital signal. The ADC 1206 outputs the digital signal to the downscaler 1210 and to the residual generator 1216. The downscaler 1210 downscales the digital signal to produce a downscaled digital signal. The downscaler 1210 outputs the downscaled digital signal to the encoder-decoder 1212. The encoderdecoder 1212 encodes and then decodes the downscaled digital signal and generates correction data 1220 to account for encode-decode errors. The encoder-decoder 1212, more specifically encoder functionality of the encoder-decoder 1212, generates an encoded base signal 1222. Although, in this specific example, encoder and decoder functionality is combined into the encoder-decoder 1212, the encoder and decoder may be separated in other examples. The encoder-decoder 1212 outputs a decoded rendition of the encoded base signal 1222 to the upscaler 1214, which upscales the downscaled digital signal and generates a predicted rendition of the digital signal. The upscaler 1214 outputs the predicted rendition of the digital signal to the residual generator 1216. The residual generator 1216 generates residuals based on the digital signal output by the ADC 1206 and the predicted rendition of the digital signal output by the upscaler 1214. For example, the residual generator 1216 may generate the residuals based on differences between the digital signal and the predicted rendition of the digital signal. The residual generator 1216 outputs the residuals to the residual encoder 1218. The residual encoder 1218 encodes the residuals generated by the residual generator 1216 to generate encoded residuals 1224. Thus, a signal (for example an image signal, a video signal, a LIDAR signal, a RADAR signal, a sound signal, a signal from a thermal sensor, etc.) is received and then encoded while the signal is in digital format. The signal may be encoded in various different ways. For example, the encoding may use VC-6 or a version of VC-6 that is suitable for the particular type of signal. This involves creating a tiered hierarchy by using a downscaling process, for example involving the downscaler 1210. The downscaling process may be linear or non-linear. The generated echelons of data that result from the hierarchical encoding process are used to predict higher level(s) and the resulting residual data is encoded (for example to generate the encoded residuals 1224). However, as explained above, the downsampling process is a computationally costly process, particularly because the process accesses significant amounts of memory. Although a non-linear downscaling process is minimises aliasing, ringing and loss of high frequencies, a non-linear downscaling process is particularly computationally costly in this sense. In accordance with examples that will now be described, the tiered hierarchy of echelons is created while the signal is still in the analog domain. This may be particularly effective for certain types of sensor and / or analog signal, or may generally be particularly effective for all types of sensor and / or analog signal. For example, certain non-linear filters may be easier to implement in the analog domain than in the digital domain. In particular, downsampling in the digital domain may involve significant amounts of matrix multiplication and in the region of 30% of the overall processing of an encoder. Measures may be taken to enable matrix multiplication to be performed efficiently. However, with, for example, 12 x 12 matrix multiplication, a significant amount of operations are still performed. The digital domain may then handle actions to reconstruct the signal for each level of quality, such as upsampling (to reconstruct a predicted rendition of the signal at a higher level of quality), residual generation, transform, and encoding. Computation and / or memory savings are particularly effective in such examples. Sensors may be edge devices that may be high-volume, low-cost devices. Additionally, power savings are particularly effective in such examples since sensors and / or edge devices are likely to be running on battery or an otherwise limited power source. In accordance with such examples, one or more low-pass filters (LPFs) are applied, in the analog domain, before one or more ADCs are used to convert the one or more analog low-pass-filtered signals into one or more digital low-pass-filtered signals. Such filtering limits the bandwidth of the analog signal. This enables the ADC(s) to use a lower sampling rate during digitisation without risking aliasing. In this way, the analog signal is prepared for efficient digitisation at a lower sampling rate and is better prepared for hierarchical processing in the digital domain and hierarchical encoding in general. Additionally, the analog processing in the analog domain may enable operations to be performed efficiently in the analog domain, which would be complicated to perform in the digital domain. By creating a hierarchical signal in the analog domain, signal compression and the encoding procedure overlaps both the analog and digital domains, rather than being purely in the digital domain. Additionally, the tiered hierarchy may be created with lower latency in the analog domain than in the digital domain. Referring to Figure 13, there is shown a schematic block diagram of an example of a heterogeneous analog-digital signal processing system 1300. The example heterogeneous analog-digital signal processing system 1300 shown in Figure 13 corresponds generally to the signal processing system 1200 shown in Figure 12. Reference signs used in Figure 13 are the same as those used in Figure 12 for the same or similar features, but incremented by 100. The heterogeneous analog-digital signal processing system 1300 may be used to implement various heterogeneous analog-digital signal processing methods, such as those described herein. In the example signal processing system 1200, the analog domain 1204 comprised only the sensor 1202. In contrast, the analog domain 1304 of the example heterogeneous analog-digital signal processing system 1300 comprises a sensor 1302 and an LPF 1303. More specifically, whereas, in the example signal processing system 1200, the sensor 1202 outputs an analog signal only to the ADC 1206, in the example heterogeneous analog-digital signal processing system 1300, the sensor 1302 outputs an analog signal to both the LPF 1303 and the ADC 1306. Additionally, whereas, in the example signal processing system 1200, the ADC 1206 obtains an analog signal only from the sensor 1202, in the example heterogeneous analog-digital signal processing system 1300, the ADC 1306 obtains an analog signal from the sensor 1302 and an analog low-pass-filtered signal from the LPF 1303. Further, whereas, in the example signal processing system 1200, the ADC 1206 outputs a digital signal to the downscaler 1210 and the residual generator 1216, in the example heterogeneous analog-digital signal processing system 1300, the ADC 1306 outputs a digital signal (a digitised version of the analog signal output by the sensor 1302) to the residual generator 1316 and a digital low-pass-filtered signal (a digitised version of the analog low-pass-filtered signal output by the LPF 1303) to the encoderdecoder 1312. Moreover, whereas the digital domain 1208 in the example signal processing system 1200 comprises the downscaler 1210, the digital domain 1308 in the example heterogeneous analog-digital signal processing system 1300 does not comprise a downscaler. In use, the sensor 1302 outputs an analog signal to the LPF 1303 and to the ADC 1306. The LPF 1303 low-pass-filters the analog signal to generate an analog low-pass-filtered signal and outputs the analog low-pass-filtered signal to the ADC 1306. The analog low-pass-filtered signal has a smaller bandwidth than a bandwidth of the analog signal, as a consequence of the low-pass filtering by the LPF 1303. The ADC 1306 converts the analog signal obtained from the sensor 1302 into a digital signal and outputs the digital signal to the residual generator 1316. The ADC 1306 also converts the analog low-pass-filtered signal obtained from the LPF 1303 into a digital low-pass-filtered signal and outputs the digital low-pass-filtered signal to the encoder-decoder 1312. The encoder-decoder 1312 encodes and then decodes the digital low-pass-filtered signal and may generate correction data 1320 to account for encode-decode errors. The encoder-decoder 1312, more specifically encoder functionality of the encoder-decoder 1312, generates an encoded base signal 1322. Although, in this specific example, encoder and decoder functionality is combined into the encoder-decoder 1312, the encoder and decoder may be separated in other examples. The encoder-decoder 1312 outputs the digital low-pass-filtered signal to the upscaler 1314, which upscales the digital low-pass-filtered signal and generates a predicted rendition of the digital signal. The upscaler 1314 outputs the predicted rendition of the digital signal to the residual generator 1316. The residual generator 1316 generates residuals based on the digital signal output by the ADC 1306 and the predicted rendition of the digital signal output by the upscaler 1314. For example, the residual generator 1316 may generate the residuals based on differences between the digital signal and the predicted rendition of the digital signal. The residual generator 1316 outputs the residuals to the residual encoder 1318. The residual encoder 1318 encodes the residuals generated by the residual generator 1316 to generate encoded residuals 1324. Thus, a heterogeneous analog-digital signal processing method may be performed, for example in the example heterogeneous analog-digital signal processing system 1300, in which an analog signal may be obtained from the sensor 1302. More generally, one or more analog signals may be obtained from one or more sensors 1302. The analog signal may be low-pass-filtered using the LPF 1303 to produce an analog low-pass-filtered signal. More generally, one or more analog signals may be low-pass-filtered using one or more LPFs 1303 to produce one or more analog low-pass-filtered signals. The analog signal may be converted into a digital signal using the ADC 1306. More generally, one or more analog signals may be converted into one or more digital signals using one or more ADCs 1306. The analog low-pass-filtered signal may be converted into a digital low-pass-filtered signal using the ADC 1306. More generally, one or more analog low-pass-filtered signals may be converted into one or more digital low-pass-filtered signals using one or more ADCs 1306. The digital signal and the digital low-pass-filtered signal may be provided to a hierarchical encoder. More generally, the one or more digital signals and the one or more low-pass-filtered signals may be provided to one or more hierarchical encoders. The hierarchical encoder may comprise some or all of the encoder-decoder 1312, the upscaler 1314, the residual generator 1316, and the residual encoder 1318. The hierarchical encoder may output some or all of the correction data 1320, the encoded base signal 1322, and the encoded residuals 1324. The method may be performed by various different types of apparatus. An example of such an apparatus is an edge device. An edge device is a computing device at an edge of a network, for example at an edge of a service provider network. A computer program may be configured to perform the method. The hierarchical encoder may comprise a VC-6 hierarchical encoder, an LCEVC hierarchical encoder, and / or any other type of hierarchical encoder. The sensor 1302 may be any type of sensor. For example, the sensor 1302 may comprise a thermal sensor, a microphone, a camera, and / or any other type of sensor. The analog signal may comprise an image signal, a video signal, an audio signal, a Light Detection and Ranging (LIDAR) signal, a Radio Detection and Ranging (RADAR) signal, a temperature signal, a volumetric signal, and / or any other type of signal. The analog signal may be, or may comprise, a multimodal analog signal. A multimodal signal may also be referred to as a “combined” signal. A multimodal analog signal may comprise multiple component signals of different types. For example, a multimodal analog signal may comprise a temperature component signal and an audio component signal. The temperature component signal may represent temperature readings, for example. The audio component signal may represent an audio recording, for example. The multimodal analog signal may comprise a first component analog signal obtained from a first sensor 1302. The multimodal analog signal may comprise a second component analog signal obtained from a second, different sensor 1302. The first component analog signal may be a first type of analog signal, and the second component analog signal may be a second, different type of analog signal. For example, the first component analog signal may be a temperature-type analog signal obtained from a thermal sensor 1302, and the second component may be an audio-type analog signal obtained from a microphone 1302. The LPF 1303 may be operable to low-pass-filter multiple different types of analog signal. For example, the LPF 1303 may be operable to low-pass-filter both temperature-type analog signals and audio-type analog signals. This may reduce the number of LPFs 1303 used in the example heterogeneous analog-digital signal processing system 1300. In some examples, different LPFs 1303 are used to low-pass-filter different types of analog signal. For example, one LPF 1303 may be used to low-pass-filter temperature-type analog signals, another LPF 1303 may be used to low-pass-filter audio-type analog signals, and so on. An analog signal may be routed to an appropriate LPF 1303 based on its signal type. This may optimise the low-pass-filtering performed by each LPF 1303 and / or may increase throughput via parallelisation. The LPF 1303 may be, or may comprise, a non-linear LPF. Using a non-linear LPF may minimise, or at least reduce, aliasing, ringing and / or loss of high frequencies. The LPF 1303 may be, or may comprise, a linear LPF. Using a non-linear LPF may be less computationally costly, for example in terms of memory access, then using a non-linear LPF. The hierarchical encoder may be configured to generate a set of encoded residuals (for example the encoded residuals 1324) by: (i) upscaling (for example using the upscaler 1314) the digital low-pass-filtered signal to generate a predicted rendition of the digital signal; (ii) generating (for example using the residual generator 1316) a set of residuals by comparing the digital signal to the predicted rendition of the digital signal; and (iii) encoding (for example using the residual encoder 1318) the set of residuals to generate the set of encoded residuals. The hierarchical encoder (examples of hierarchical coding have been described above with reference to Figures 1 to 11) may be configured to generate an encoded signal (for example the encoded base signal 1322) without downscaling the digital signal and / or the digital low-pass-filtered signal. In particular, whereas the digital domain 1208 of the example signal processing system 1200 comprises a downscaler 1210, the digital domain 1308 of the example heterogeneous analog-digital signal processing system 1300 does not comprise a downscaler. Instead, in the example heterogeneous analog-digital signal processing system 1300, the analog domain 1304 comprises an LPF 1303. In this, sense the LPF 1303 functions as a downsampler in the analog domain 1304. Thus, description of use of an LPF may be read as performing a downsample operation in an analog domain on an analog signal. The analog signal and the analog low-pass-filtered signal may be converted into the digital signal and the digital low-pass-filtered signal respectively using a single ADC 1306. This may reduce the number of ADCs 1306 used in the example heterogeneous analog-digital signal processing system 1300. One of the analog signal and the analog low-pass-filtered signal may be converted using the single ADC 1306 before the other of the analog signal and the analog low-pass-filtered signal is converted using the single ADC 1306. This pipelines the ADC 1306. The analog signal may be converted into the digital signal using a first ADC 1306, and the analog low-pass-filtered signal may be converted into the digital low-pass-filtered signal using a second ADC 1306. This may optimise the analog-to-digital conversion performed by each ADC 1306 and / or may increase throughput via parallelisation. In accordance with examples, a data stream may be provided that comprises data indicative that an encoded signal has been generated in accordance with a method as described herein. The data stream may comprise some or all of the correction data 1320, the encoded base signal 1322, and the encoded residuals 1324. The encoded signal may be, or may comprise, the encoded base signal 1322. The indicative data may take various different forms. For instance, the indicative data may be a flag where, for example, a value of “1” indicates that the encoded signal has been generated in accordance with a method as described herein and a value of “0” indicates that the encoded signal has not been generated in accordance with a method as described herein. The indicative data may be comprised in metadata. The metadata may indicate how the data stream and / or the encoded signal is to be decoded. An apparatus is also provided. The apparatus comprises a sensor (for example the sensor 1302) configured to output an analog signal. The apparatus comprises an LPF (for example the LPF 1303) configured to low-pass-filter the analog signal to produce an analog low-pass-filtered signal. The apparatus comprises an ADC (for example the ADC 1306) configured to convert: (i) the analog signal into a digital signal; and (ii) the analog low-pass-filtered signal into a digital low-pass-filtered signal. The apparatus comprises a hierarchical encoder configured to obtain the digital signal and the digital low-pass-filtered signal from the analog-to-digital converter and to generate an encoded signal. Although only one instance of each element of the example heterogeneous analog-digital signal processing system 1300 is shown in Figure 13, there may be one or more instances of some or all elements of the heterogeneous analog-digital signal processing system 1300 in other examples. For instance, although only a single sensor 1302, a single LPF 1303 and a single ADC 1306 is shown in Figure 13, one or more instances of such elements may be provided in other examples. Additionally, although references are made to “an” analog signal or to “a” digital signal, one or more such signals may be present in other examples. Thus, any references to “a” or “an” entity in the heterogeneous analog-digital signal processing system 1300, and / or in other example systems and methods described herein, should be understood to include one or more than one such entity unless the context indicates otherwise. In the example signal processing system 1200 shown in Figure 12, there is a symmetry between the downscaler 1210 and the upscaler 1214 in that both are in the digital domain 1208. In contrast, in the example heterogeneous analog-digital signal processing system 1300 shown in Figure 13, there is an asymmetry because the LPF 1303 is in the analog domain 1304, the upscaler 1314 is in the digital domain 1308, and the digital domain 1308 does not comprise a downscaler. The example heterogeneous analog-digital signal processing system 1300 is still effective even though the LPF 1303 is in the analog domain 1304 and the upscaler 1314 is in the digital domain 1308. Referring to Figure 14, there is shown a schematic block diagram of an example of a heterogeneous analog-digital signal processing system 1400. The example heterogeneous analog-digital signal processing system 1400 shown in Figure 14 corresponds generally to the heterogeneous analog-digital signal processing system 1300 shown in Figure 13. Reference signs used in Figure 14 are the same as those used in Figure 13 for the same or similar features, but incremented by 100. Similar to the example heterogeneous analog-digital signal processing system 1300, the example heterogeneous analog-digital signal processing system 1400 comprises a sensor 1402 in the analog domain 1404, an ADC 1406, and a digital domain 1408. However, whereas, in the example heterogeneous analog-digital signal processing system 1300, only a single LPF 1303 is depicted, in the example heterogeneous analog-digital signal processing system 1400, multiple LPFs 1403-A, ..., 1403-N are shown. In this example, each LPF 1403-A, ..., 1403-N obtains an analog signal from the sensor 1402 and outputs a respective analog low-pass-filtered signal to the ADC 1406. The digital domain comprises a hierarchical encoder 1426. The hierarchical encoder 1426 may comprise the same or similar elements to those shown in the digital domain 1308 in the example heterogeneous analog-digital signal processing system 1300. For example, the hierarchical encoder 1426 may comprise one or more encoder decoders, one or more upscalers, one or more residual generators, and one or more residual encoders. Thus, the analog signal (output by the sensor 1402) may be low-pass-filtered (for example by a second LPF (not shown in Figure 14)) to produce a further analog low-pass-filtered signal. The further analog low-pass-filtered signal may have a different bandwidth from a bandwidth of an analog low-pass-filtered signal output by the first LPF 1403-A. For example, the further analog low-pass-filtered signal may have a smaller bandwidth than the bandwidth of the analog low-pass-filtered signal. The further analog low-pass-filtered signal may be converted (for example by the ADC 1406) into a further digital low-pass-filtered signal. The further digital low-pass-filtered signal may be provided to the hierarchical encoder 1426. The hierarchical encoder (examples of hierarchical coding have been described above with reference to Figures 1 to 11) 1426 may be configured to generate a further set of encoded residuals by: (i) upscaling (for example using an upscaler) the further digital low-pass-filtered signal to generate a predicted rendition of the digital low-pass-filtered signal; (ii) generating (for example using a residual generator) a further set of residuals by comparing the digital low-pass-filtered signal to the predicted rendition of the digital low-pass-filtered signal; and encoding the further set of residuals to generate the further set of encoded residuals. The analog signal (output by the sensor 1402) may be low-pass-filtered (for example by a third LPF (not shown in Figure 14)) to produce an additional analog low-pass-filtered signal. The additional analog low-pass-filtered signal may have a different bandwidth from the bandwidth of the analog low-pass-filtered signal and may have a different bandwidth from a bandwidth of the further analog low-pass-filtered signal. The additional analog low-pass-filtered signal may have a smaller bandwidth than the bandwidth of the analog low-pass-filtered signal. The additional analog low-pass-filtered signal may have a smaller bandwidth than the bandwidth of the further analog low-pass-filtered signal. The additional analog low-pass-filtered signal may be converted (for example using the ADC 1406) into an additional digital low-pass-filtered signal. The additional digital low-pass-filtered signal may be provided to the hierarchical encoder 1426. Referring to Figure 15, there is shown a schematic block diagram of an example of a heterogeneous analog-digital signal processing system 1500. The example heterogeneous analog-digital signal processing system 1500 shown in Figure 15 corresponds generally to the heterogeneous analog-digital signal processing system 1400 shown in Figure 14. Reference signs used in Figure 15 are the same as those used in Figure 14 for the same or similar features, but incremented by 100. Similar to the example heterogeneous analog-digital signal processing system 1400, the example heterogeneous analog-digital signal processing system 1500 comprises multiple LPFs 1503-A, ..., 1503-N. However, whereas, in the example heterogeneous analog-digital signal processing system 1400, each LPF 1403-A, ..., 1403-N obtains an analog signal from the sensor 1402 as its input, in the example heterogeneous analog-digital signal processing system 1500, the sensor 1502 outputs an analog signal to the first LPF 1503-A and to the ADC 1506. The first LPF 1503-A outputs an analog low-pass-filtered signal to the second LPF (not shown in Figure 15) and to the ADC 1506. The second LPF (not shown in Figure 15) low-pass-filters the analog low-pass-filtered signal to generate a further analog low-pass-filtered signal and outputs the further analog low-pass-filtered signal to a third LPF (not shown in Figure 15) and to the ADC 1506. The third LPF (not shown in Figure 15) low-pass-filters the further analog low-pass-filtered signal to generate an additional analog low-pass-filtered signal and outputs the additional analog low-pass-filtered signal to a fourth LPF (not shown in Figure 15) and to the ADC 1506. This continues until the final LPF 1503-N outputs to the ADC 1506. Thus, the analog low-pass-filtered signal (output by the first LPF 1503-A) may be low-pass filtered (for example by the second LPF (not shown in Figure 15)) to produce a further analog low-pass-filtered signal. The further analog low-pass-filtered signal may have a different bandwidth from a bandwidth of the analog low-pass-filtered signal. The further analog low-pass-filtered signal may have a smaller bandwidth than the bandwidth of the analog low-pass-filtered signal. The further analog low-pass-filtered signal may be converted (for example by the ADC 1506) into a further digital low-pass-filtered signal. The further digital low-pass-filtered signal may be provided to the hierarchical encoder 1526. The hierarchical encoder 1526 may be configured to generate a further set of encoded residuals by: (i) upscaling (for example using an upscaler) the further digital low-pass-filtered signal to generate a predicted rendition of the digital low-pass-filtered signal; (ii) generating (for example using a residual generator) a further set of residuals by comparing the digital low-pass-filtered signal to the predicted rendition of the digital low-pass-filtered signal; and encoding the further set of residuals to generate the further set of encoded residuals. The further analog low-pass-filtered signal (output by the second LPF (not shown in Figure 15)) may be low-pass-filtered to produce an additional analog low-pass-filtered signal. The additional analog low-pass-filtered signal may have a different bandwidth from the bandwidth of the analog low-pass-filtered signal. For example, the additional analog low-pass-filtered signal may have a smaller bandwidth than the bandwidth of the analog low-pass-filtered signal. The additional analog low-pass-filtered signal may have a different bandwidth from a bandwidth of the further analog low-pass-filtered signal. For example, the additional analog low-pass-filtered signal may have a smaller bandwidth than the bandwidth of the further analog low-pass-filtered signal. The additional analog low-pass-filtered signal may be converted (for example by the ADC 1506) into an additional digital low-pass-filtered signal. The additional digital low-pass-filtered signal may be provided to the hierarchical encoder 1526. Although not shown in Figure 15, instead of the further analog low-pass-filtered signal (output by the second LPF (not shown in Figure 15)) being low-pass-filtered to produce the additional analog low-pass-filtered signal, the analog low-pass-filtered signal (output by the first LPF 1503-A) or the analog signal (output by the sensor 1502) may be low-pass-filtered to produce the additional analog low-pass-filtered signal. The hierarchical encoder 1526 may be configured to generate a further set of encoded residuals by: upscaling the further digital low-pass-filtered signal to generate a predicted rendition of the digital low-pass-filtered signal; generating a further set of residuals by comparing the digital low-pass-filtered signal to the predicted rendition of the digital low-pass-filtered signal; and encoding the further set of residuals to generate the further set of encoded residuals. Referring to Figure 16, there is shown a schematic block diagram of an example of a heterogeneous analog-digital signal processing system 1600. The example heterogeneous analog-digital signal processing system 1600 shown in Figure 16 corresponds generally to the heterogeneous analog-digital signal processing system 1500 shown in Figure 15. Reference signs used in Figure 16 are the same as those used in Figure 15 for the same or similar features, but incremented by 100. Similar to the example heterogeneous analog-digital signal processing system 1500, the example heterogeneous analog-digital signal processing system 1600 comprises multipleLPFs 1603-A, ..., 1603-N. However, whereas, in the example heterogeneous analog-digital signal processing system 1500, only a single ADC 1506 is present, in the example heterogeneous analog-digital signal processing system 1600 there are multiple ADCs 1606-A, ..., 1606-N. Each ADC 1606-A, ..., 1606-N may have a different configuration. For example, the first ADC 1606-A may convert to high resolution at 1440p, the second ADC (not shown in Figure 16) may convert to medium resolution at 720p, the third ADC (not shown in Figure 16) may convert to low resolution at 360p, and so on. In this specific example, the sensor 1602 and the first LPF 1603-A output to the first ADC 1606-A and the first ADC 1606-A outputs to the hierarchical encoder 1626. Also, in this specific example, each subsequent LPF (including the final LPF 1603-N) outputs to a respective ADC (including the final ADC 1606-N for the final LPF 1603-N), and each of those ADCs outputs to the hierarchical encoder 1626. In other examples, the sensor 1602 and the first LPF 1603-A may output to different respective LPFs. Additionally, although the example heterogeneous analog-digital signal processing system 1600 corresponds closely to the example heterogeneous analogdigital signal processing system 1400 in that the second and subsequent LPFs obtain input from an immediately higher-level LPF instead of the sensor 1602, the multi-ADC configuration of the example heterogeneous analog-digital signal processing system 1600 may be used in the example heterogeneous analog-digital signal processing system 1400. Thus, in the example heterogeneous analog-digital signal processing systems 1400 and 1500 shown in Figures 14 and 15 respectively, a single ADC 1406, 1506 is used to digitise multiple echelons of analog signals. Thus, the example heterogeneous analog-digital signal processing systems 1400 and 1500 shown in Figures 14 and 15 comprise one or more shared processing units (for example the single ADC 1406, 1506) for different echelons and / or signal types and / or different component signals of a multimodal signal having different types of component signal. In contrast, in the example heterogeneous analog-digital signal processing systems 1600 shown in Figure 16, multiple ADCs 1606-A, ..., 1606-N are used to digitise the multiple echelons of analog signals. Each such ADC 1606-A, ..., 1606-N may digitise one or more echelons of analog signals. For instance, in the example heterogeneous analog-digital signal processing systems 1600 shown in Figure 16, the first ADC 1606-A digitises two echelons of analog signal, and each other ADC digitises a single respective echelon of analog signal. Referring to Figure 17, there is shown a schematic block diagram of an example of a heterogeneous analog-digital signal processing system 1700. The example heterogeneous analog-digital signal processing system 1700 shown in Figure 17 corresponds generally to the heterogeneous analog-digital signal processing system 1300 shown in Figure 13. Reference signs used in Figure 17 are the same as those used in Figure 13 for the same or similar features, but incremented by 400. However, whereas Figure 13 shows various hierarchical encoder components, the hierarchical encoder is represented as a single hierarchical encoder component 1726 in Figure 17. A signal processing method may be performed, for example in the example heterogeneous analog-digital signal processing system 1700. An analog signal is obtained (for example from the sensor 1702). The analog signal is processed in the analog domain 1704 (for example by an analog signal processor 1728) to generate an analog processed signal. The analog processed signal has a reduced amount of analog signal content relative to the analog signal. The analog signal content may comprise frequency content, amplitude content, temporal content, or otherwise. For example, the analog signal processor 1728 may comprise an LPF, may perform logarithmic averaging, may perform non-linear filtering, may perform processing that would otherwise use a neural network and / or complicated convolutions and / or computations, etc. The analog signal and the analog processed signal are converted (for example by the ADC 1706) into a digital signal and a digital processed signal respectively. The digital signal and the digital processed signal are provided to a hierarchical encoder 1726. Referring to Figure 18, there is shown a schematic block diagram of an example of a heterogeneous analog-digital signal processing system 1800. Figure 18 represents a tiered hierarchy with multiple levels of quality. In this example, the tiered hierarchy comprises three levels of quality. However, the example heterogeneous analog-digital signal processing system 1800 may be extended to include one or more additional levels of quality. In Figure 18, elements of the example heterogeneous analog-digital signal processing system 1800 at a lowest level of quality in the tiered hierarchy are denoted using a suffix “-1”, elements at a medium level of quality in the tiered hierarchy are denoted using a suffix “-2”, and elements at a highest level of quality in the tiered hierarchy are denoted using a suffix “-3”. The sensor 1802 outputs an analog signal to an LPF 1803-2 at the medium level of quality. The LPF 1803-2 low-pass-filters the analog signal and outputs to an LPF 1803-1 at the lowest level of quality. The LPF 1803-1 low-pass-filters the analog low-pass-filtered signal and outputs to an ADC 1806-1 at the lowest level of quality. The ADC 1806-1 generates a digital low-pass-filtered signal 1830-1 at the lowest level of quality. The digital low-pass-filtered signal 1830-1 is upscaled by an upscaler 1814-1 at the lowest level of quality, which outputs an upscaled digital low-pass-filtered signal to a residual generator 1816-2 at the medium level of quality. The LPF 1803-2 at the medium level of quality also outputs to an ADC 1806-2 at the medium level of quality. The ADC 1806-2 generates an additional digital low-pass-filtered signal 1830-2 at the medium level of quality. The additional digital low-pass-filtered signal 1830-2 is output to the residual generator 1816-2 at the medium level of quality. The residual generator 1816-2 outputs residuals 1824-2 at the medium level of quality. The additional digital low-pass-filtered signal 1830-2 is also upscaled by an upscaler 1814-2 at the medium level of quality. The upscaler 1814-2 outputs to a residual generator 1816-3 at the highest level of quality. The sensor 1802 also outputs the analog signal to an ADC 1802-3 at the highest level of quality, which generates a digital signal 1830-3 at the highest level of quality. The digital signal 1830-3 is input to the residual generator 1816-3. The residual generator 1816-3 outputs residuals 1824-3 at the highest level of quality. The digital low-pass-filtered signal 1830-1 may be output to a recipient device. The recipient device may use the digital low-pass-filtered signal 1830-1 at the lowest level of quality. For example, the recipient device may decode the digital low-pass-filtered signal 1830-1 and output the decoded signal for display. The residuals 1824-2 may also be output to the recipient device. The recipient device may upscale the digital low-pass-filtered signal 1830-1 (for example, mirroring the upscaling performed by the upscaler 1814-1) to generate a predicted rendition of the signal 1830-2 at the medium level of quality. The recipient device may use the residuals 1824-2 to improve the predicted rendition of the signal 1830-2. The residuals 1824-3 may also be output to the recipient device. The recipient device may upscale the improved predicted rendition of the signal 1830-2 (for example, mirroring the upscaling performed by the upscaler 1814-2) to generate a predicted rendition of the signal 1830-3 at the highest level of quality. The recipient device may use the residuals 1824-3 to improve the predicted rendition of the signal 1830-3. In general, a multimodal signal may be obtained by combining component signals from multiple sensors. The sensors may include different types of sensor. Alternatively, a multimodal signal may be obtained from a single sensor. For example, the sensor may be able to sense temperature, light, sound, etc. In some examples, the multimodal signal itself is low-pass-filtered. In other examples, the component signals of the multimodal signal are low-pass-filtered, using one or more LPFs, and the component signals are combined into the multimodal signal. Such combining may occur in the digital domain after the component signals have been converted to digital by the ADC(s). Referring to Figure 19, there is shown a schematic block diagram of an example of an apparatus 1900. In an example, the apparatus 1900 comprises an encoder. In another example, the apparatus 1900 comprises a decoder. In other examples, the apparatus 1900 comprises neither an encoder nor a decoder but is configured to communicate with an encoder and / or a decoder. Examples of apparatus 1900 include, but are not limited to, a mobile computer, a personal computer system, a wireless device, base station, phone device, desktop computer, laptop, notebook, netbook computer, mainframe computer system, handheld computer, workstation, network computer, application server, storage device, a consumer electronics device such as a camera, camcorder, mobile device, video game console, handheld video game device, an XR headset, or in general any type of computing or electronic device. In this example, the apparatus 1900 comprises one or more processors 1901 configured to process information and / or instructions. The one or more processors 1901 may comprise a CPU. The one or more processors 1901 are coupled with a bus 1902. Operations performed by the one or more processors 1901 may be carried out by hardware and / or software. The one or more processors 1901 may comprise multiple colocated processors or multiple disparately located processors. In this example, the apparatus 1900 comprises computer-useable volatile memory 1903 configured to store information and / or instructions for the one or more processors 1901. The computer-useable volatile memory 1903 is coupled with the bus 1902. The computer-useable volatile memory 1903 may comprise random access memory (RAM). In this example, the apparatus 1900 comprises computer-useable non-volatile memory 1904 configured to store information and / or instructions for the one or more processors 1901. The computer-useable non-volatile memory 1904 is coupled with the bus 1902. The computer-useable non-volatile memory 1904 may comprise read-only memory (ROM). In this example, the apparatus 1900 comprises one or more data-storage units 1905 configured to store information and / or instructions. The one or more data-storage units 1905 are coupled with the bus 1902. The one or more data-storage units 1905 may for example comprise a magnetic or optical disk and disk drive or a solid-state drive (SSD). In this example, the apparatus 1900 comprises one or more input / output (I / O) devices 1906 configured to communicate information to and / or from the one or more processors 1901. The one or more I / O devices 1906 are coupled with the bus 1902. The one or more I / O devices 1906 may comprise at least one network interface. The at least one network interface may enable the apparatus 1900 to communicate via one or more data communications networks. Examples of data communications networks include, but are not limited to, the Internet and a Local Area Network (LAN). The one or more EO devices 1906 may enable a user to provide input to the apparatus 1900 via one or more input devices (not shown). The one or more input devices may include for example a remote control, one or more physical buttons etc. The one or more I / O devices 1906 may enable information to be provided to a user via one or more output devices (not shown). The one or more output devices may for example include a display screen. Various other entities are depicted for the apparatus 1900. For example, when present, an operating system 1907, image processing module 1908, one or more further modules 1909, and data 1910 are shown as residing in one, or a combination, of the computer-usable volatile memory 1903, computer-usable non-volatile memory 1904 and the one or more data-storage units 1905. The data signal processing module 1908 may be implemented by way of computer program code stored in memory locations within the computer-usable non-volatile memory 1904, computer-readable storage media within the one or more data-storage units 1905 and / or other tangible computer-readable storage media. Examples of tangible computer-readable storage media include, but are not limited to, an optical medium (e.g., CD-ROM, DVD-ROM or Blu-ray), flash memory card, floppy or hard disk or any other medium capable of storing computer-readable instructions such as firmware or microcode in at least one ROM or RAM or Programmable ROM (PROM) chips or as an Application Specific Integrated Circuit (ASIC). The apparatus 1900 may therefore comprise a data signal processing module 1908 which can be executed by the one or more processors 1901. The data signal processing module 1908 can be configured to include instructions to implement at least some of the operations described herein. During operation, the one or more processors 1901 launch, run, execute, interpret or otherwise perform the instructions in the signal processing module 1908. Although at least some aspects of the examples described herein with reference to the drawings comprise computer processes performed in processing systems or processors, examples described herein also extend to computer programs, for example computer programs on or in a carrier, adapted for putting the examples into practice. 5 The carrier may be any entity or device capable of carrying the program. It will be appreciated that the apparatus 1900 may comprise more, fewer and / or different components from those depicted in Figure 19. The apparatus 1900 may be located in a single location or may be distributed in multiple locations. Such locations may be local or remote. 10 The techniques described herein may be implemented in software or hardware, or may be implemented using a combination of software and hardware. They may include configuring an apparatus to carry out and / or support any or all of techniques described herein. It is to be understood that any feature described in relation to any one 15 embodiment may be used alone, or in combination with other features described, and may also be used in combination with one or more features of any other of the embodiments, or any combination of any other of the embodiments. Furthermore, equivalents and modifications not described above may also be employed without departing from the scope of the invention, which is defined in the accompanying claims.

Claims

1. A heterogeneous analog-digital signal processing method, comprising: obtaining an analog signal from a sensor;low-pass-filtering the analog signal using a low-pass filter to produce an analog low-pass-filtered signal;converting, using an analog-to-digital converter: the analog signal into a digital signal; and the analog low-pass-filtered signal into a digital low-pass-filtered signal;andproviding the digital signal and the digital low-pass-filtered signal to a hierarchical encoder.

2. A method according to claim 1, wherein the analog signal is a multimodal analog signal.

3. A method according to claim 2, wherein the multimodal analog signal comprises a first component analog signal obtained from a first sensor and a second component analog signal obtained from a second, different sensor, wherein the first component analog signal is a first type of analog signal, and wherein the second component analog signal is a second, different type of analog signal.

4. A method according to any of claims 1 to 3, wherein the low-pass-filter is operable to low-pass-filter multiple different types of analog signal.

5. A method according to any of claims 1 to 4, wherein the low-pass filter is a nonlinear low-pass-filter.

6. A method according to any of claims 1 to 4, wherein the low-pass filter is a linear low-pass-filter.

7. A method according to any of claims 1 to 6, wherein the hierarchical encoder is configured to generate a set of encoded residuals by:upscaling the digital low-pass-filtered signal to generate a predicted rendition of the digital signal;generating a set of residuals by comparing the digital signal to the predicted rendition of the digital signal; andencoding the set of residuals to generate the set of encoded residuals.

8. A method according to any of claims 1 to 7, wherein the hierarchical encoder is configured to generate an encoded signal without downscaling the digital signal and / or the digital low-pass-filtered signal.

9. A method according to any of claims 1 to 8, wherein the analog signal and the analog low-pass-filtered signal are converted into the digital signal and the digital low-pass-filtered signal respectively using a single analog-to-digital converter.

10. A method according to claim 9, wherein one of the analog signal and the analog low-pass-filtered signal is converted using the single analog-to-digital converter before the other of the analog signal and the analog low-pass-filtered signal is converted using the single analog-to-digital converter.

11. A method according to any of claims 1 to 8, wherein the analog signal is converted into the digital signal using a first analog-to-digital converter, and wherein the analog low-pass-filtered signal is converted into the digital low-pass-filtered signal using a second analog-to-digital converter.

12. A method according to any of claims 1 to 11, wherein the method comprises: low-pass-filtering the analog signal or the analog low-pass-filtered signal to produce a further analog low-pass-filtered signal, the further analog low-pass-filtered signal having a different bandwidth from a bandwidth of the analog low-pass-filtered signal;converting the further analog low-pass-filtered signal into a further digital low-pass-filtered signal; andproviding the further digital low-pass-filtered signal to the hierarchical encoder.

13. A method according to claim 12, wherein the further analog low-pass-filtered signal has a smaller bandwidth than the bandwidth of the analog low-pass-filtered signal.

14. A method according to claim 12 or 13, wherein the hierarchical encoder is configured to generate a further set of encoded residuals by:upscaling the further digital low-pass-filtered signal to generate a predicted rendition of the digital low-pass-filtered signal;generating a further set of residuals by comparing the digital low-pass-filtered signal to the predicted rendition of the digital low-pass-filtered signal; andencoding the further set of residuals to generate the further set of encoded residuals.

15. A method according to any of claims 12 to 14, wherein the method comprises: low-pass-filtering the analog signal, the analog low-pass-filtered signal, or the further analog low-pass-filtered signal to produce an additional analog low-pass-filtered signal, the additional analog low-pass-filtered signal having a different bandwidth from the bandwidth of the analog low-pass-filtered signal and having a different bandwidth from a bandwidth of the further analog low-pass-filtered signal;converting the additional analog low-pass-filtered signal into an additional digital low-pass-filtered signal; andproviding the additional digital low-pass-filtered signal to the hierarchical encoder.

16. A method according to claim 15, wherein the additional analog low-pass-filtered signal has a smaller bandwidth than the bandwidth of the analog low-pass-filtered signal and has a smaller bandwidth than the bandwidth of the further analog low-pass-filtered signal.17.A method according to any of claims1 to 16, wherein the analog signalcomprises an image signal.

18. A method according to any of claims 1 to 17, wherein the analog signalcomprises a video signal.

19. A method according to any of claims 1 to 18, wherein the analog signalcomprises an audio signal.

20. A method according to any of claims 1 to 19, wherein the analog signalcomprises a LIDAR signal.

21. A method according to any of claims 1 to 20, wherein the analog signalcomprises a RADAR signal.

22. A method according to any of claims 1 to 21, wherein the analog signalcomprises a temperature signal.

23. A method according to any of claims 1 to 21, wherein the analog signalcomprises a volumetric signal.

24. A method according to any of claims 1 to 23, wherein the sensor comprises a thermal sensor.

25. A method according to any of claims 1 to 24, wherein the hierarchical encoder comprises a VC-6 hierarchical encoder.

26. A method according to any of claims 1 to 25, wherein the hierarchical encoder comprises an LCEVC hierarchical encoder.

27. A method according to any of claims 1 to 26, wherein the method is performed by an edge device.

28. A signal processing method, comprising:obtaining an analog signal;processing the analog signal in an analog domain to generate an analog processed signal, the analog processed signal having a reduced amount of analog signal content relative to the analog signal;converting the analog signal and the analog processed signal into a digital signal and a digital processed signal respectively; andproviding the digital signal and the digital processed signal to a hierarchical encoder.

29. Apparatus configured to perform a method according to any of claims 1 to 28.

30. Apparatus comprising:a sensor configured to output an analog signal;a low-pass filter configured to low-pass-filter the analog signal to produce an analog low-pass-filtered signal;an analog-to-digital converter configured to convert:the analog signal into a digital signal; andthe analog low-pass-filtered signal into a digital low-pass-filtered signal; anda hierarchical encoder configured to obtain the digital signal and the digital low-pass-filtered signal from the analog-to-digital converter and to generate an encoded signal.

31. A computer program configured to perform a method according to any of claims 1 to 28.

32. A data stream comprising data indicative that an encoded signal has been generated in accordance with a method according to any of claims 1 to 28.

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